In [1]:
%run ADL_sea.ipynb
Number of input:  3
Number of output:  2
Number of batch:  100
All labeled
100% (100 of 100) |######################| Elapsed Time: 0:02:38 ETA:  00:00:00

=== Performance result ===
Accuracy:  91.77878787878788 (+/-) 7.774645046654492
Testing Loss:  0.2475769944075081 (+/-) 0.17538645165333314
Precision:  0.9180847507481505
Recall:  0.9177878787878788
F1 score:  0.9169805927240723
Testing Time:  0.004532293839888139 (+/-) 0.006663265097464228
Training Time:  1.5917126915671609 (+/-) 0.14680636875031983


=== Average network evolution ===
Total hidden node:  10.525252525252526 (+/-) 4.409226859880036
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=18, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=18, out_features=2, bias=True)
)
No. of inputs : 3
No. of nodes : 18
No. of parameters : 110
Voting weight:  [1.0]
100% (100 of 100) |######################| Elapsed Time: 0:02:47 ETA:  00:00:00

=== Performance result ===
Accuracy:  91.63737373737374 (+/-) 7.9348685280406555
Testing Loss:  0.248694378581613 (+/-) 0.17716825433551067
Precision:  0.9166299192863216
Recall:  0.9163737373737374
F1 score:  0.9155568032410182
Testing Time:  0.004933330747816298 (+/-) 0.007703368873455288
Training Time:  1.6848876957941537 (+/-) 0.05137850635048642


=== Average network evolution ===
Total hidden node:  7.828282828282828 (+/-) 4.917584206199083
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=15, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=15, out_features=2, bias=True)
)
No. of inputs : 3
No. of nodes : 15
No. of parameters : 92
Voting weight:  [1.0]
100% (100 of 100) |######################| Elapsed Time: 0:02:45 ETA:  00:00:00

=== Performance result ===
Accuracy:  91.60000000000001 (+/-) 7.599774052994318
Testing Loss:  0.2515264848115468 (+/-) 0.17756957551505967
Precision:  0.9168156406672259
Recall:  0.916
F1 score:  0.9149588010903454
Testing Time:  0.005166641389480745 (+/-) 0.007896823959998392
Training Time:  1.6596386336316966 (+/-) 0.03279892095374525


=== Average network evolution ===
Total hidden node:  11.323232323232324 (+/-) 4.8675970111747775
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=20, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=20, out_features=2, bias=True)
)
No. of inputs : 3
No. of nodes : 20
No. of parameters : 122
Voting weight:  [1.0]
100% (100 of 100) |######################| Elapsed Time: 0:02:43 ETA:  00:00:00

=== Performance result ===
Accuracy:  92.51313131313131 (+/-) 6.005917056522474
Testing Loss:  0.24561232618159717 (+/-) 0.16810259909722203
Precision:  0.9251274808780803
Recall:  0.9251313131313131
F1 score:  0.9246032873653983
Testing Time:  0.005292723877261383 (+/-) 0.007490605758028644
Training Time:  1.6460770284286652 (+/-) 0.024563431714747435


=== Average network evolution ===
Total hidden node:  10.767676767676768 (+/-) 4.199154034158735
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=18, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=18, out_features=2, bias=True)
)
No. of inputs : 3
No. of nodes : 18
No. of parameters : 110
Voting weight:  [1.0]
100% (100 of 100) |######################| Elapsed Time: 0:02:30 ETA:  00:00:00

=== Performance result ===
Accuracy:  92.28787878787878 (+/-) 6.410885199082313
Testing Loss:  0.24687982346824924 (+/-) 0.17060163623546878
Precision:  0.9229782102311964
Recall:  0.9228787878787879
F1 score:  0.9222569323384725
Testing Time:  0.005219413776590367 (+/-) 0.007124591540430939
Training Time:  1.5094771722350457 (+/-) 0.18872308541102747


=== Average network evolution ===
Total hidden node:  14.505050505050505 (+/-) 4.628357339717398
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=22, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=22, out_features=2, bias=True)
)
No. of inputs : 3
No. of nodes : 22
No. of parameters : 134
Voting weight:  [1.0]
Mean Accuracy:  92.18530612244899
Std Accuracy:  6.840025391419877
Hidden Node mean 11.046938775510204
Hidden Node std:  5.072116735636615
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
50% labeled
100% (100 of 100) |######################| Elapsed Time: 0:01:03 ETA:  00:00:00

=== Performance result ===
Accuracy:  90.789898989899 (+/-) 8.582357931627218
Testing Loss:  0.27071312513917384 (+/-) 0.17561097302458126
Precision:  0.9088370624415407
Recall:  0.9078989898989899
F1 score:  0.9066235507421124
Testing Time:  0.004466723914098258 (+/-) 0.006532980041181871
Training Time:  0.6369590277623649 (+/-) 0.015738429264258714


=== Average network evolution ===
Total hidden node:  12.282828282828282 (+/-) 3.954387421536252
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=20, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=20, out_features=2, bias=True)
)
No. of inputs : 3
No. of nodes : 20
No. of parameters : 122
Voting weight:  [1.0]
100% (100 of 100) |######################| Elapsed Time: 0:01:03 ETA:  00:00:00

=== Performance result ===
Accuracy:  90.72323232323232 (+/-) 8.656903921823464
Testing Loss:  0.27458650581162386 (+/-) 0.1740398787779756
Precision:  0.9085636748176429
Recall:  0.9072323232323233
F1 score:  0.9058076866681034
Testing Time:  0.003949100320989435 (+/-) 0.0054398273542768695
Training Time:  0.6349737909105089 (+/-) 0.03386464524745355


=== Average network evolution ===
Total hidden node:  11.777777777777779 (+/-) 3.9557030956481447
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=19, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=19, out_features=2, bias=True)
)
No. of inputs : 3
No. of nodes : 19
No. of parameters : 116
Voting weight:  [1.0]
100% (100 of 100) |######################| Elapsed Time: 0:01:02 ETA:  00:00:00

=== Performance result ===
Accuracy:  90.5838383838384 (+/-) 8.925784995845577
Testing Loss:  0.2734414122697681 (+/-) 0.17615246614542596
Precision:  0.9083339242387399
Recall:  0.9058383838383839
F1 score:  0.9040395948406204
Testing Time:  0.0038553488374960545 (+/-) 0.005011258493050731
Training Time:  0.6251076447843301 (+/-) 0.013943595998541655


=== Average network evolution ===
Total hidden node:  9.545454545454545 (+/-) 3.939627032730991
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=16, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=16, out_features=2, bias=True)
)
No. of inputs : 3
No. of nodes : 16
No. of parameters : 98
Voting weight:  [1.0]
100% (100 of 100) |######################| Elapsed Time: 0:01:02 ETA:  00:00:00

=== Performance result ===
Accuracy:  89.12828282828282 (+/-) 10.965464983476375
Testing Loss:  0.28966213378942374 (+/-) 0.1976872619703256
Precision:  0.8954560942810239
Recall:  0.8912828282828282
F1 score:  0.8885168794829359
Testing Time:  0.0036543017686015426 (+/-) 0.004977736961850179
Training Time:  0.6244075201978587 (+/-) 0.018499661143103075


=== Average network evolution ===
Total hidden node:  5.484848484848484 (+/-) 3.804207230660659
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=12, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=12, out_features=2, bias=True)
)
No. of inputs : 3
No. of nodes : 12
No. of parameters : 74
Voting weight:  [1.0]
100% (100 of 100) |######################| Elapsed Time: 0:01:02 ETA:  00:00:00

=== Performance result ===
Accuracy:  91.03333333333335 (+/-) 8.046430917186234
Testing Loss:  0.27042536900350544 (+/-) 0.17240731220981606
Precision:  0.9118056129039311
Recall:  0.9103333333333333
F1 score:  0.9089480132814006
Testing Time:  0.004172349216962102 (+/-) 0.006375912688472921
Training Time:  0.6282619876090927 (+/-) 0.01449281374351998


=== Average network evolution ===
Total hidden node:  8.777777777777779 (+/-) 3.909469352390929
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=16, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=16, out_features=2, bias=True)
)
No. of inputs : 3
No. of nodes : 16
No. of parameters : 98
Voting weight:  [1.0]
Mean Accuracy:  90.71448979591837
Std Accuracy:  8.773965096534292
Hidden Node mean 9.614285714285714
Hidden Node std:  4.609240939766148
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
25% labeled
100% (100 of 100) |######################| Elapsed Time: 0:00:31 ETA:  00:00:00

=== Performance result ===
Accuracy:  85.8787878787879 (+/-) 13.171466503161046
Testing Loss:  0.3355905680042325 (+/-) 0.20088427162846714
Precision:  0.8686284666298859
Recall:  0.8587878787878788
F1 score:  0.8526720595334817
Testing Time:  0.004018894349685823 (+/-) 0.007422354144938157
Training Time:  0.3165863668075716 (+/-) 0.011347629563175804


=== Average network evolution ===
Total hidden node:  3.595959595959596 (+/-) 2.173853776313337
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=8, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 3
No. of nodes : 8
No. of parameters : 50
Voting weight:  [1.0]
100% (100 of 100) |######################| Elapsed Time: 0:00:31 ETA:  00:00:00

=== Performance result ===
Accuracy:  88.98484848484847 (+/-) 10.402693548204294
Testing Loss:  0.30224385067369 (+/-) 0.1865478362791381
Precision:  0.8939479485291837
Recall:  0.8898484848484849
F1 score:  0.8870408605837202
Testing Time:  0.004059622986148102 (+/-) 0.006205225922172628
Training Time:  0.3140897269200797 (+/-) 0.009588336907420426


=== Average network evolution ===
Total hidden node:  7.474747474747475 (+/-) 3.0890599869373956
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=13, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=13, out_features=2, bias=True)
)
No. of inputs : 3
No. of nodes : 13
No. of parameters : 80
Voting weight:  [1.0]
100% (100 of 100) |######################| Elapsed Time: 0:00:31 ETA:  00:00:00

=== Performance result ===
Accuracy:  88.6030303030303 (+/-) 10.67551200054729
Testing Loss:  0.31178097810709116 (+/-) 0.18547889571966275
Precision:  0.8907633510458334
Recall:  0.8860303030303031
F1 score:  0.8828870303009299
Testing Time:  0.0041606594817806975 (+/-) 0.006289658859874122
Training Time:  0.3164662688669532 (+/-) 0.010643389254065955


=== Average network evolution ===
Total hidden node:  9.393939393939394 (+/-) 2.7370196200187005
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=15, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=15, out_features=2, bias=True)
)
No. of inputs : 3
No. of nodes : 15
No. of parameters : 92
Voting weight:  [1.0]
100% (100 of 100) |######################| Elapsed Time: 0:00:31 ETA:  00:00:00

=== Performance result ===
Accuracy:  89.87979797979798 (+/-) 8.677545384414982
Testing Loss:  0.2955809903114733 (+/-) 0.16807242257865118
Precision:  0.9017287144777161
Recall:  0.8987979797979798
F1 score:  0.896649173317882
Testing Time:  0.0038671806605175287 (+/-) 0.005369903093762293
Training Time:  0.3167310459445221 (+/-) 0.014903033282089268


=== Average network evolution ===
Total hidden node:  9.303030303030303 (+/-) 2.4056102166047686
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=14, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=14, out_features=2, bias=True)
)
No. of inputs : 3
No. of nodes : 14
No. of parameters : 86
Voting weight:  [1.0]
100% (100 of 100) |######################| Elapsed Time: 0:00:31 ETA:  00:00:00

=== Performance result ===
Accuracy:  89.86464646464647 (+/-) 9.795632021120156
Testing Loss:  0.29094100367240233 (+/-) 0.17510082852756947
Precision:  0.9008028037053026
Recall:  0.8986464646464647
F1 score:  0.8967111551403387
Testing Time:  0.003875404897362295 (+/-) 0.005090700992513294
Training Time:  0.31470381370698564 (+/-) 0.01212607451383069


=== Average network evolution ===
Total hidden node:  9.343434343434344 (+/-) 3.1435732617118726
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=15, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=15, out_features=2, bias=True)
)
No. of inputs : 3
No. of nodes : 15
No. of parameters : 92
Voting weight:  [1.0]
Mean Accuracy:  88.87755102040816
Std Accuracy:  10.539332846989975
Hidden Node mean 7.844897959183673
Hidden Node std:  3.54165078680975
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
Infinite Delay
 87% (87 of 100) |####################   | Elapsed Time: 0:00:00 ETA:   0:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  63.03636363636363 (+/-) 7.379461732655587
Testing Loss:  0.6052399422183181 (+/-) 0.04699389579780294
Precision:  0.3973583140495867
Recall:  0.6303636363636363
F1 score:  0.4874474690025041
Testing Time:  0.003083043628268772 (+/-) 0.00533707125976596
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  5.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 3
No. of nodes : 5
No. of parameters : 32
Voting weight:  [1.0]
 88% (88 of 100) |####################   | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  71.99292929292929 (+/-) 7.030672375065245
Testing Loss:  0.5526376333501604 (+/-) 0.037641174592493255
Precision:  0.7848681601356409
Recall:  0.719929292929293
F1 score:  0.6650847450042424
Testing Time:  0.003424545731207337 (+/-) 0.006346452159494795
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  7.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=7, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 3
No. of nodes : 7
No. of parameters : 44
Voting weight:  [1.0]
 95% (95 of 100) |#####################  | Elapsed Time: 0:00:00 ETA:   0:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  63.03636363636363 (+/-) 7.379461732655587
Testing Loss:  0.5988696964100154 (+/-) 0.06342261963084407
Precision:  0.3973583140495867
Recall:  0.6303636363636363
F1 score:  0.4874474690025041
Testing Time:  0.0029216270254115865 (+/-) 0.005074020210044609
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  4.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=4, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 3
No. of nodes : 4
No. of parameters : 26
Voting weight:  [1.0]
 92% (92 of 100) |#####################  | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  63.5060606060606 (+/-) 6.988559455928966
Testing Loss:  0.617055017538745 (+/-) 0.03424064358230706
Precision:  0.6383223715619244
Recall:  0.6350606060606061
F1 score:  0.5079429566450805
Testing Time:  0.0033042310464261758 (+/-) 0.006985716885186875
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  4.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=4, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 3
No. of nodes : 4
No. of parameters : 26
Voting weight:  [1.0]
 97% (97 of 100) |###################### | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  63.91313131313131 (+/-) 7.405030319536977
Testing Loss:  0.5881666679574986 (+/-) 0.04031304123673675
Precision:  0.7535737633047251
Recall:  0.6391313131313131
F1 score:  0.5079612440573773
Testing Time:  0.0030230921928328697 (+/-) 0.005692165210033363
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  5.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 3
No. of nodes : 5
No. of parameters : 32
Voting weight:  [1.0]
Mean Accuracy:  65.08816326530612
Std Accuracy:  8.057255742235903
Hidden Node mean 5.0
Hidden Node std:  1.0954451150103321
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
In [2]:
%run ADL_hyperplane.ipynb
Number of input:  4
Number of output:  2
Number of batch:  120
All labeled
100% (120 of 120) |######################| Elapsed Time: 0:02:36 ETA:  00:00:00

=== Performance result ===
Accuracy:  92.1327731092437 (+/-) 2.8999886769902825
Testing Loss:  0.29674529378153697 (+/-) 0.050821148293742575
Precision:  0.9213279873338623
Recall:  0.9213277310924369
F1 score:  0.9213276551809617
Testing Time:  0.003980576491155545 (+/-) 0.006082166285759226
Training Time:  1.3117703650178028 (+/-) 0.23992105310726106


=== Average network evolution ===
Total hidden node:  4.680672268907563 (+/-) 0.5931368415133208
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=4, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 4
No. of nodes : 5
No. of parameters : 37
Voting weight:  [1.0]
100% (120 of 120) |######################| Elapsed Time: 0:02:38 ETA:  00:00:00

=== Performance result ===
Accuracy:  92.49747899159662 (+/-) 3.8435466417585946
Testing Loss:  0.2826259513111675 (+/-) 0.06514220485021761
Precision:  0.9249865953720963
Recall:  0.9249747899159664
F1 score:  0.9249739198597343
Testing Time:  0.004233091819186171 (+/-) 0.006687962856221777
Training Time:  1.3240773757966626 (+/-) 0.09279423517343063


=== Average network evolution ===
Total hidden node:  2.7899159663865545 (+/-) 0.46515470598648884
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=4, out_features=4, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 4
No. of nodes : 4
No. of parameters : 30
Voting weight:  [1.0]
100% (120 of 120) |######################| Elapsed Time: 0:02:36 ETA:  00:00:00

=== Performance result ===
Accuracy:  92.43109243697484 (+/-) 3.387960589771074
Testing Loss:  0.2825475792173578 (+/-) 0.06330441064276668
Precision:  0.9244009525197152
Recall:  0.9243109243697479
F1 score:  0.9243059525514109
Testing Time:  0.004109683157015247 (+/-) 0.006169050609558756
Training Time:  1.3082366069825757 (+/-) 0.05542289684948646


=== Average network evolution ===
Total hidden node:  2.6050420168067228 (+/-) 0.48884166629408354
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=4, out_features=3, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 4
No. of nodes : 3
No. of parameters : 23
Voting weight:  [1.0]
100% (120 of 120) |######################| Elapsed Time: 0:02:34 ETA:  00:00:00

=== Performance result ===
Accuracy:  91.93781512605041 (+/-) 4.986233006596636
Testing Loss:  0.2997847918201895 (+/-) 0.052510714023436
Precision:  0.9195238845219994
Recall:  0.9193781512605042
F1 score:  0.9193698375265501
Testing Time:  0.004584490752019802 (+/-) 0.006870784035579428
Training Time:  1.288882339701933 (+/-) 0.04074106203722367


=== Average network evolution ===
Total hidden node:  6.80672268907563 (+/-) 0.4353557595626426
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=4, out_features=7, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 4
No. of nodes : 7
No. of parameters : 51
Voting weight:  [1.0]
100% (120 of 120) |######################| Elapsed Time: 0:02:30 ETA:  00:00:00

=== Performance result ===
Accuracy:  92.27983193277313 (+/-) 3.3536780218551714
Testing Loss:  0.2935772026035966 (+/-) 0.048129877616022
Precision:  0.9227994477380908
Recall:  0.922798319327731
F1 score:  0.9227983673627906
Testing Time:  0.003982660149325843 (+/-) 0.005868431937428219
Training Time:  1.25931769058484 (+/-) 0.019634580561862392


=== Average network evolution ===
Total hidden node:  4.831932773109243 (+/-) 0.37392597413927703
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=4, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 4
No. of nodes : 5
No. of parameters : 37
Voting weight:  [1.0]
Mean Accuracy:  92.47389830508475
Std Accuracy:  2.824540770803164
Hidden Node mean 4.34406779661017
Hidden Node std:  1.6077957525271356
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
50% labeled
100% (120 of 120) |######################| Elapsed Time: 0:01:17 ETA:  00:00:00

=== Performance result ===
Accuracy:  91.23529411764706 (+/-) 4.488202803939806
Testing Loss:  0.32004584259345753 (+/-) 0.05771400334108622
Precision:  0.9123541891109385
Recall:  0.9123529411764706
F1 score:  0.9123529958653975
Testing Time:  0.004331318270258543 (+/-) 0.006192188497448879
Training Time:  0.646723781313215 (+/-) 0.020113962363550005


=== Average network evolution ===
Total hidden node:  9.117647058823529 (+/-) 0.3927166872453084
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=4, out_features=9, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 4
No. of nodes : 9
No. of parameters : 65
Voting weight:  [1.0]
100% (120 of 120) |######################| Elapsed Time: 0:01:17 ETA:  00:00:00

=== Performance result ===
Accuracy:  91.65798319327732 (+/-) 3.9211663459132384
Testing Loss:  0.30582883716130455 (+/-) 0.07184320426854718
Precision:  0.9165890200747565
Recall:  0.9165798319327731
F1 score:  0.9165790198499257
Testing Time:  0.0039309874302198905 (+/-) 0.005929414859571515
Training Time:  0.6492844469407025 (+/-) 0.022532867927356436


=== Average network evolution ===
Total hidden node:  2.403361344537815 (+/-) 0.8630542572415308
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=4, out_features=2, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=2, out_features=2, bias=True)
)
No. of inputs : 4
No. of nodes : 2
No. of parameters : 16
Voting weight:  [1.0]
100% (120 of 120) |######################| Elapsed Time: 0:01:19 ETA:  00:00:00

=== Performance result ===
Accuracy:  91.63949579831933 (+/-) 3.759273541644278
Testing Loss:  0.3139606143246178 (+/-) 0.06598343254852429
Precision:  0.9164031181770563
Recall:  0.9163949579831933
F1 score:  0.9163942149492789
Testing Time:  0.004368551638947816 (+/-) 0.006166941205697606
Training Time:  0.6587371044800061 (+/-) 0.0290957574274117


=== Average network evolution ===
Total hidden node:  7.6722689075630255 (+/-) 0.4693862199586199
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=4, out_features=7, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 4
No. of nodes : 7
No. of parameters : 51
Voting weight:  [1.0]
100% (120 of 120) |######################| Elapsed Time: 0:01:17 ETA:  00:00:00

=== Performance result ===
Accuracy:  91.43529411764705 (+/-) 4.322883962568386
Testing Loss:  0.3110736668610773 (+/-) 0.06571033262169357
Precision:  0.9143534667980341
Recall:  0.9143529411764706
F1 score:  0.9143529867791536
Testing Time:  0.004059346784062746 (+/-) 0.006134376617332956
Training Time:  0.6471098130490599 (+/-) 0.028292310142209023


=== Average network evolution ===
Total hidden node:  4.697478991596639 (+/-) 0.47729317045925806
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=4, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 4
No. of nodes : 5
No. of parameters : 37
Voting weight:  [1.0]
100% (120 of 120) |######################| Elapsed Time: 0:01:16 ETA:  00:00:00

=== Performance result ===
Accuracy:  91.24285714285713 (+/-) 4.387617630946224
Testing Loss:  0.3104899478058855 (+/-) 0.06589411784119345
Precision:  0.9124408670108138
Recall:  0.9124285714285715
F1 score:  0.9124274879423412
Testing Time:  0.004052428638233858 (+/-) 0.005918169271976022
Training Time:  0.6345747999784326 (+/-) 0.016783416843650017


=== Average network evolution ===
Total hidden node:  4.563025210084033 (+/-) 0.5288111839513367
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=4, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 4
No. of nodes : 5
No. of parameters : 37
Voting weight:  [1.0]
Mean Accuracy:  91.63050847457627
Std Accuracy:  3.651094762094321
Hidden Node mean 5.688135593220339
Hidden Node std:  2.460748456194848
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
25% labeled
100% (120 of 120) |######################| Elapsed Time: 0:00:38 ETA:  00:00:00

=== Performance result ===
Accuracy:  89.22689075630252 (+/-) 6.802541510705579
Testing Loss:  0.3611920959308368 (+/-) 0.0915200572070845
Precision:  0.8922693559170953
Recall:  0.8922689075630252
F1 score:  0.892268961363977
Testing Time:  0.0037483427704883224 (+/-) 0.005806303064268548
Training Time:  0.31993593087717265 (+/-) 0.011380686806736444


=== Average network evolution ===
Total hidden node:  2.689075630252101 (+/-) 0.8275497817889198
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=4, out_features=2, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=2, out_features=2, bias=True)
)
No. of inputs : 4
No. of nodes : 2
No. of parameters : 16
Voting weight:  [1.0]
100% (120 of 120) |######################| Elapsed Time: 0:00:39 ETA:  00:00:00

=== Performance result ===
Accuracy:  85.35294117647061 (+/-) 10.329754880310379
Testing Loss:  0.42153964723859516 (+/-) 0.15506028011071907
Precision:  0.8535836903111857
Recall:  0.8535294117647059
F1 score:  0.8535254756709769
Testing Time:  0.003788747707334887 (+/-) 0.005751274527775746
Training Time:  0.3282131788109531 (+/-) 0.014660267415140772


=== Average network evolution ===
Total hidden node:  2.134453781512605 (+/-) 0.34113921227200744
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=4, out_features=2, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=2, out_features=2, bias=True)
)
No. of inputs : 4
No. of nodes : 2
No. of parameters : 16
Voting weight:  [1.0]
100% (120 of 120) |######################| Elapsed Time: 0:00:39 ETA:  00:00:00

=== Performance result ===
Accuracy:  89.1983193277311 (+/-) 7.8392031750068965
Testing Loss:  0.3487726337268573 (+/-) 0.09833892094931501
Precision:  0.8921083468128961
Recall:  0.8919831932773109
F1 score:  0.8919728178555902
Testing Time:  0.004155715974439092 (+/-) 0.0061491273277764985
Training Time:  0.323298662650485 (+/-) 0.015492256556860663


=== Average network evolution ===
Total hidden node:  5.647058823529412 (+/-) 0.4778846120374095
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=4, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 4
No. of nodes : 6
No. of parameters : 44
Voting weight:  [1.0]
100% (120 of 120) |######################| Elapsed Time: 0:00:39 ETA:  00:00:00

=== Performance result ===
Accuracy:  89.6672268907563 (+/-) 6.532917415437297
Testing Loss:  0.34111762622825237 (+/-) 0.08930110145580447
Precision:  0.8968777185912759
Recall:  0.896672268907563
F1 score:  0.8966567772192012
Testing Time:  0.0041334007968421745 (+/-) 0.006064069973112024
Training Time:  0.3240250859941755 (+/-) 0.019959393970618987


=== Average network evolution ===
Total hidden node:  5.100840336134453 (+/-) 0.8239580431432123
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=4, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 4
No. of nodes : 6
No. of parameters : 44
Voting weight:  [1.0]
100% (120 of 120) |######################| Elapsed Time: 0:00:38 ETA:  00:00:00

=== Performance result ===
Accuracy:  89.60420168067226 (+/-) 6.869985810389653
Testing Loss:  0.34015305027240467 (+/-) 0.09252488654875712
Precision:  0.8960422946093396
Recall:  0.8960420168067227
F1 score:  0.8960420594661951
Testing Time:  0.004444519010912471 (+/-) 0.006743340048539339
Training Time:  0.31771476529225584 (+/-) 0.011803125587923206


=== Average network evolution ===
Total hidden node:  5.2521008403361344 (+/-) 0.8522682281349981
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=4, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 4
No. of nodes : 6
No. of parameters : 44
Voting weight:  [1.0]
Mean Accuracy:  88.89406779661017
Std Accuracy:  7.373673881036386
Hidden Node mean 4.16271186440678
Hidden Node std:  1.616849657600534
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
Infinite Delay
 96% (116 of 120) |##################### | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  72.33361344537816 (+/-) 2.6420732027252227
Testing Loss:  0.6355133692757422 (+/-) 0.007198295135825457
Precision:  0.7233570865729828
Recall:  0.7233361344537815
F1 score:  0.7233327448894931
Testing Time:  0.0032512260084392643 (+/-) 0.00626940860053139
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  4.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=4, out_features=4, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 4
No. of nodes : 4
No. of parameters : 30
Voting weight:  [1.0]
 96% (116 of 120) |##################### | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  59.162184873949585 (+/-) 1.6315683848349878
Testing Loss:  0.6379734493103348 (+/-) 0.006076872644249988
Precision:  0.7501416886814637
Recall:  0.5916218487394957
F1 score:  0.5142552738281043
Testing Time:  0.0033873670241411995 (+/-) 0.005715510497239788
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  6.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=4, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 4
No. of nodes : 6
No. of parameters : 44
Voting weight:  [1.0]
 91% (110 of 120) |####################  | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  75.77563025210084 (+/-) 3.1907156908112344
Testing Loss:  0.5996591348608001 (+/-) 0.011213254420422465
Precision:  0.7654742181221579
Recall:  0.7577563025210085
F1 score:  0.7560300598258421
Testing Time:  0.0035610980346423237 (+/-) 0.006303100160741767
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  7.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=4, out_features=7, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 4
No. of nodes : 7
No. of parameters : 51
Voting weight:  [1.0]
 91% (110 of 120) |####################  | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  70.87058823529411 (+/-) 1.5395269895192576
Testing Loss:  0.6155929665605561 (+/-) 0.005537559541700183
Precision:  0.7936655836740353
Recall:  0.7087058823529412
F1 score:  0.6861784988827916
Testing Time:  0.003395433185481224 (+/-) 0.006682583876897558
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  5.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=4, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 4
No. of nodes : 5
No. of parameters : 37
Voting weight:  [1.0]
 99% (119 of 120) |##################### | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  62.73277310924369 (+/-) 1.6649177557448698
Testing Loss:  0.6535219209534782 (+/-) 0.005991285437134587
Precision:  0.7242308977263545
Recall:  0.627327731092437
F1 score:  0.5818200776223557
Testing Time:  0.0037464875133097673 (+/-) 0.007757471119442078
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  4.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=4, out_features=4, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 4
No. of nodes : 4
No. of parameters : 30
Voting weight:  [1.0]
Mean Accuracy:  68.1820338983051
Std Accuracy:  6.587017220507145
Hidden Node mean 5.2
Hidden Node std:  1.16619037896906
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
In [3]:
%run ADL_weather.ipynb
Number of input:  8
Number of output:  2
Number of batch:  18
All labeled
100% (18 of 18) |########################| Elapsed Time: 0:00:21 ETA:  00:00:00

=== Performance result ===
Accuracy:  71.28823529411765 (+/-) 3.5106363346334284
Testing Loss:  0.5447211931733524 (+/-) 0.0446058537811574
Precision:  0.6906965967572122
Recall:  0.7128823529411765
F1 score:  0.6881400855055277
Testing Time:  0.0024969437543083638 (+/-) 0.0005008489421290735
Training Time:  1.2536747595843147 (+/-) 0.017878871100577967


=== Average network evolution ===
Total hidden node:  7.117647058823529 (+/-) 0.47058823529411764
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=9, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 9
No. of parameters : 101
Voting weight:  [1.0]
100% (18 of 18) |########################| Elapsed Time: 0:00:21 ETA:  00:00:00

=== Performance result ===
Accuracy:  71.8294117647059 (+/-) 2.9511243532079234
Testing Loss:  0.541138116051169 (+/-) 0.032101969186811824
Precision:  0.6969625860043405
Recall:  0.7182941176470589
F1 score:  0.6904015172559538
Testing Time:  0.002802414052626666 (+/-) 0.0005158063836586612
Training Time:  1.2623528732972986 (+/-) 0.020901295079057226


=== Average network evolution ===
Total hidden node:  5.176470588235294 (+/-) 0.3812200410828153
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 6
No. of parameters : 68
Voting weight:  [1.0]
100% (18 of 18) |########################| Elapsed Time: 0:00:21 ETA:  00:00:00

=== Performance result ===
Accuracy:  71.9470588235294 (+/-) 3.4337627529575507
Testing Loss:  0.5376740010345683 (+/-) 0.04287728052833601
Precision:  0.6984483033781103
Recall:  0.7194705882352941
F1 score:  0.6906652570618736
Testing Time:  0.005447780384736902 (+/-) 0.010121669242366281
Training Time:  1.2635347422431498 (+/-) 0.027621743061962033


=== Average network evolution ===
Total hidden node:  8.058823529411764 (+/-) 0.23529411764705882
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=9, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 9
No. of parameters : 101
Voting weight:  [1.0]
100% (18 of 18) |########################| Elapsed Time: 0:00:21 ETA:  00:00:00

=== Performance result ===
Accuracy:  71.12941176470588 (+/-) 2.849512797548205
Testing Loss:  0.5566502301131978 (+/-) 0.041308765620337654
Precision:  0.686943425161547
Recall:  0.7112941176470589
F1 score:  0.677979788144965
Testing Time:  0.005564381094539867 (+/-) 0.01034121925005352
Training Time:  1.2668095195994657 (+/-) 0.017857610201188787


=== Average network evolution ===
Total hidden node:  7.470588235294118 (+/-) 0.6056253024110001
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=9, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 9
No. of parameters : 101
Voting weight:  [1.0]
100% (18 of 18) |########################| Elapsed Time: 0:00:21 ETA:  00:00:00

=== Performance result ===
Accuracy:  71.41764705882352 (+/-) 2.9945509914072943
Testing Loss:  0.5408432150588316 (+/-) 0.03668811205538584
Precision:  0.6910045857091238
Recall:  0.7141764705882353
F1 score:  0.6779933092886355
Testing Time:  0.004869236665613511 (+/-) 0.009516292548315352
Training Time:  1.2627655898823458 (+/-) 0.025571197862122575


=== Average network evolution ===
Total hidden node:  6.0588235294117645 (+/-) 0.5391265523477459
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=8, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 8
No. of parameters : 90
Voting weight:  [1.0]
Mean Accuracy:  71.44875
Std Accuracy:  3.258718833759672
Hidden Node mean 6.8
Hidden Node std:  1.1224972160321824
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
50% labeled
100% (18 of 18) |########################| Elapsed Time: 0:00:10 ETA:  00:00:00

=== Performance result ===
Accuracy:  68.8 (+/-) 3.6320469030399862
Testing Loss:  0.587289950426887 (+/-) 0.029988123771981533
Precision:  0.6418107060755336
Recall:  0.688
F1 score:  0.6075096455571712
Testing Time:  0.0028778945698457606 (+/-) 0.00046956275967809775
Training Time:  0.6353792442994959 (+/-) 0.018875720767487236


=== Average network evolution ===
Total hidden node:  5.9411764705882355 (+/-) 0.23529411764705882
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 6
No. of parameters : 68
Voting weight:  [1.0]
100% (18 of 18) |########################| Elapsed Time: 0:00:10 ETA:  00:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  68.60000000000001 (+/-) 4.105806505282918
Testing Loss:  0.6037568239604726 (+/-) 0.04618104923153804
Precision:  0.470596
Recall:  0.686
F1 score:  0.5582396204033215
Testing Time:  0.0026931902941535503 (+/-) 0.0006663862507812772
Training Time:  0.6336122540866628 (+/-) 0.011524882364522614


=== Average network evolution ===
Total hidden node:  2.1176470588235294 (+/-) 0.32218973970892123
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=2, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=2, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 2
No. of parameters : 24
Voting weight:  [1.0]
100% (18 of 18) |########################| Elapsed Time: 0:00:10 ETA:  00:00:00

=== Performance result ===
Accuracy:  69.8 (+/-) 3.145304623795698
Testing Loss:  0.5758455080144546 (+/-) 0.031821676907984965
Precision:  0.668073010473592
Recall:  0.698
F1 score:  0.6260377468356028
Testing Time:  0.002924021552590763 (+/-) 0.0004133982146249562
Training Time:  0.635547539767097 (+/-) 0.017425422864793136


=== Average network evolution ===
Total hidden node:  6.294117647058823 (+/-) 0.4556450995538137
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 6
No. of parameters : 68
Voting weight:  [1.0]
100% (18 of 18) |########################| Elapsed Time: 0:00:10 ETA:  00:00:00

=== Performance result ===
Accuracy:  70.21176470588236 (+/-) 3.11993256958453
Testing Loss:  0.5660208586384269 (+/-) 0.03237144529787478
Precision:  0.674552825912901
Recall:  0.7021176470588235
F1 score:  0.639975261245551
Testing Time:  0.005148032132317038 (+/-) 0.009450001721434198
Training Time:  0.6321498646455652 (+/-) 0.013014187224119275


=== Average network evolution ===
Total hidden node:  8.058823529411764 (+/-) 0.23529411764705882
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=9, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 9
No. of parameters : 101
Voting weight:  [1.0]
100% (18 of 18) |########################| Elapsed Time: 0:00:11 ETA:  00:00:00

=== Performance result ===
Accuracy:  70.8529411764706 (+/-) 2.8350988460947923
Testing Loss:  0.5595578113022972 (+/-) 0.034596745088238914
Precision:  0.6862030235766621
Recall:  0.7085294117647059
F1 score:  0.6517578418853633
Testing Time:  0.0049100483165067784 (+/-) 0.009256207439080035
Training Time:  0.6420904187595143 (+/-) 0.028351118303813747


=== Average network evolution ===
Total hidden node:  6.588235294117647 (+/-) 0.49215295678475035
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 6
No. of parameters : 68
Voting weight:  [1.0]
Mean Accuracy:  69.4625
Std Accuracy:  3.523470412817454
Hidden Node mean 5.8125
Hidden Node std:  2.0315865105872306
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
25% labeled
100% (18 of 18) |########################| Elapsed Time: 0:00:05 ETA:  00:00:00

=== Performance result ===
Accuracy:  68.74117647058824 (+/-) 3.9422386628336556
Testing Loss:  0.6158314803067375 (+/-) 0.030927392092747884
Precision:  0.6404650433944069
Recall:  0.6874117647058824
F1 score:  0.5805847981727883
Testing Time:  0.0027942657470703125 (+/-) 0.0003813975455210023
Training Time:  0.32602426585029154 (+/-) 0.008471772957877662


=== Average network evolution ===
Total hidden node:  6.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 6
No. of parameters : 68
Voting weight:  [1.0]
100% (18 of 18) |########################| Elapsed Time: 0:00:05 ETA:  00:00:00

=== Performance result ===
Accuracy:  68.40588235294116 (+/-) 4.256824360156053
Testing Loss:  0.6017854248776155 (+/-) 0.031852880827710185
Precision:  0.6106257981255584
Recall:  0.6840588235294117
F1 score:  0.5700357135549584
Testing Time:  0.0026844108805936925 (+/-) 0.0005736784881599483
Training Time:  0.3185214154860553 (+/-) 0.01075346024773728


=== Average network evolution ===
Total hidden node:  4.9411764705882355 (+/-) 0.23529411764705882
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 5
No. of parameters : 57
Voting weight:  [1.0]
100% (18 of 18) |########################| Elapsed Time: 0:00:05 ETA:  00:00:00

=== Performance result ===
Accuracy:  68.59411764705884 (+/-) 4.108308738646784
Testing Loss:  0.5901334425982308 (+/-) 0.0328028741189406
Precision:  0.6256501010651587
Recall:  0.6859411764705883
F1 score:  0.5616446520527002
Testing Time:  0.0025049377890194163 (+/-) 0.0005033900762385822
Training Time:  0.33699708826401653 (+/-) 0.027320601358832443


=== Average network evolution ===
Total hidden node:  2.823529411764706 (+/-) 0.38122004108281526
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=2, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=2, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 2
No. of parameters : 24
Voting weight:  [1.0]
100% (18 of 18) |########################| Elapsed Time: 0:00:05 ETA:  00:00:00

=== Performance result ===
Accuracy:  69.20588235294117 (+/-) 3.3282163723912674
Testing Loss:  0.5876201917143429 (+/-) 0.02767136397522674
Precision:  0.6535588183741049
Recall:  0.6920588235294117
F1 score:  0.6129351984547017
Testing Time:  0.005266666412353516 (+/-) 0.009168843906099539
Training Time:  0.32491106145522175 (+/-) 0.012361018050410573


=== Average network evolution ===
Total hidden node:  7.176470588235294 (+/-) 0.3812200410828154
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=7, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 7
No. of parameters : 79
Voting weight:  [1.0]
100% (18 of 18) |########################| Elapsed Time: 0:00:05 ETA:  00:00:00

=== Performance result ===
Accuracy:  68.51176470588236 (+/-) 4.150532545978276
Testing Loss:  0.5968040368136238 (+/-) 0.02931462935797009
Precision:  0.6239822366053435
Recall:  0.6851176470588235
F1 score:  0.57615607694714
Testing Time:  0.005026340484619141 (+/-) 0.008980994083083465
Training Time:  0.3277925042545094 (+/-) 0.012116460751914505


=== Average network evolution ===
Total hidden node:  6.647058823529412 (+/-) 0.47788461203740945
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=7, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 7
No. of parameters : 79
Voting weight:  [1.0]
Mean Accuracy:  68.44125
Std Accuracy:  3.971199244246
Hidden Node mean 5.525
Hidden Node std:  1.565047922588954
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
Infinite Delay
N/A% (0 of 18) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  68.60000000000001 (+/-) 4.105806505282918
Testing Loss:  0.6114419172791874 (+/-) 0.042625517355778825
Precision:  0.470596
Recall:  0.686
F1 score:  0.5582396204033215
Testing Time:  0.00199278663186466 (+/-) 0.0006832930403184679
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  5.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 5
No. of parameters : 57
Voting weight:  [1.0]
N/A% (0 of 18) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  68.60000000000001 (+/-) 4.105806505282918
Testing Loss:  0.6190536688355839 (+/-) 0.04722587376596562
Precision:  0.470596
Recall:  0.686
F1 score:  0.5582396204033215
Testing Time:  0.002169482848223518 (+/-) 0.0003808956359621945
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  6.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 6
No. of parameters : 68
Voting weight:  [1.0]
N/A% (0 of 18) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  68.60000000000001 (+/-) 4.105806505282918
Testing Loss:  0.604694829267614 (+/-) 0.029736020023796664
Precision:  0.470596
Recall:  0.686
F1 score:  0.5582396204033215
Testing Time:  0.001641091178445255 (+/-) 0.0005860307650434803
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  8.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=8, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 8
No. of parameters : 90
Voting weight:  [1.0]
 55% (10 of 18) |#############           | Elapsed Time: 0:00:00 ETA:  00:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  68.60000000000001 (+/-) 4.105806505282918
Testing Loss:  0.6358883836690117 (+/-) 0.01901054610216545
Precision:  0.470596
Recall:  0.686
F1 score:  0.5582396204033215
Testing Time:  0.004104880725636202 (+/-) 0.008209694043914615
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  3.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=3, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 3
No. of parameters : 35
Voting weight:  [1.0]
 88% (16 of 18) |#####################   | Elapsed Time: 0:00:00 ETA:  00:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  68.60000000000001 (+/-) 4.105806505282918
Testing Loss:  0.6130610003190882 (+/-) 0.020320980405448164
Precision:  0.470596
Recall:  0.686
F1 score:  0.5582396204033215
Testing Time:  0.0038719317492316753 (+/-) 0.008035594198310014
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  6.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 6
No. of parameters : 68
Voting weight:  [1.0]
Mean Accuracy:  68.34375
Std Accuracy:  4.098165557600132
Hidden Node mean 5.6
Hidden Node std:  1.624807680927192
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
In [4]:
%run ADL_rfid.ipynb
Number of input:  3
Number of output:  4
Number of batch:  280
All labeled
100% (280 of 280) |######################| Elapsed Time: 0:05:58 ETA:  00:00:00

=== Performance result ===
Accuracy:  98.41146953405017 (+/-) 6.141090027784853
Testing Loss:  0.0965246818959713 (+/-) 0.17284467521388874
Precision:  0.9841308469045139
Recall:  0.9841146953405018
F1 score:  0.9840860127384184
Testing Time:  0.007006877639387671 (+/-) 0.006994731274461606
Training Time:  1.2744880790778812 (+/-) 0.022834699301133923


=== Average network evolution ===
Total hidden node:  34.01433691756272 (+/-) 10.434287058736738
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=44, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=44, out_features=4, bias=True)
)
No. of inputs : 3
No. of nodes : 44
No. of parameters : 356
Voting weight:  [1.0]
100% (280 of 280) |######################| Elapsed Time: 0:06:05 ETA:  00:00:00

=== Performance result ===
Accuracy:  97.04802867383513 (+/-) 9.853936210362207
Testing Loss:  0.13435740113645578 (+/-) 0.251883736862754
Precision:  0.9704735499687195
Recall:  0.9704802867383513
F1 score:  0.9704095529854146
Testing Time:  0.006977005244155938 (+/-) 0.006838161119704925
Training Time:  1.301266676208879 (+/-) 0.10862877520837326


=== Average network evolution ===
Total hidden node:  31.387096774193548 (+/-) 12.059915171213738
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=43, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=43, out_features=4, bias=True)
)
No. of inputs : 3
No. of nodes : 43
No. of parameters : 348
Voting weight:  [1.0]
100% (280 of 280) |######################| Elapsed Time: 0:06:12 ETA:  00:00:00

=== Performance result ===
Accuracy:  98.21720430107527 (+/-) 7.3865462849150925
Testing Loss:  0.09239974950096407 (+/-) 0.1817480334189206
Precision:  0.9822011505795067
Recall:  0.9821720430107527
F1 score:  0.9821334631042031
Testing Time:  0.007548198050495544 (+/-) 0.007267964247017353
Training Time:  1.3254282739427354 (+/-) 0.1785320461125022


=== Average network evolution ===
Total hidden node:  36.842293906810035 (+/-) 10.624085190539514
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=47, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=47, out_features=4, bias=True)
)
No. of inputs : 3
No. of nodes : 47
No. of parameters : 380
Voting weight:  [1.0]
100% (280 of 280) |######################| Elapsed Time: 0:05:54 ETA:  00:00:00

=== Performance result ===
Accuracy:  98.60860215053765 (+/-) 5.083496156334232
Testing Loss:  0.08971409343757189 (+/-) 0.1642726220065111
Precision:  0.9860681229865902
Recall:  0.9860860215053764
F1 score:  0.9860737663352921
Testing Time:  0.007476118730387807 (+/-) 0.007229839076040581
Training Time:  1.2585441143282 (+/-) 0.020427169435175932


=== Average network evolution ===
Total hidden node:  35.14336917562724 (+/-) 10.297764874404166
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=44, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=44, out_features=4, bias=True)
)
No. of inputs : 3
No. of nodes : 44
No. of parameters : 356
Voting weight:  [1.0]
100% (280 of 280) |######################| Elapsed Time: 0:05:52 ETA:  00:00:00

=== Performance result ===
Accuracy:  98.3537634408602 (+/-) 6.0645847628237055
Testing Loss:  0.0999376311377492 (+/-) 0.1833736045447648
Precision:  0.9835077967355159
Recall:  0.9835376344086022
F1 score:  0.9835094556113015
Testing Time:  0.0073757684358986475 (+/-) 0.0074610463853840205
Training Time:  1.2530899808398284 (+/-) 0.020589625964804428


=== Average network evolution ===
Total hidden node:  33.681003584229394 (+/-) 10.207982209277864
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=44, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=44, out_features=4, bias=True)
)
No. of inputs : 3
No. of nodes : 44
No. of parameters : 356
Voting weight:  [1.0]
Mean Accuracy:  98.35050359712228
Std Accuracy:  6.062120544973156
Hidden Node mean 34.313669064748204
Hidden Node std:  10.785856722717943
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
50% labeled
100% (280 of 280) |######################| Elapsed Time: 0:02:57 ETA:  00:00:00

=== Performance result ===
Accuracy:  96.4010752688172 (+/-) 11.40038194628655
Testing Loss:  0.1934991893241696 (+/-) 0.26474521303583715
Precision:  0.9640247995857536
Recall:  0.964010752688172
F1 score:  0.9640098803310996
Testing Time:  0.006720724926199964 (+/-) 0.0070109531001696914
Training Time:  0.6276418581658367 (+/-) 0.015504405852863641


=== Average network evolution ===
Total hidden node:  25.311827956989248 (+/-) 8.497916878990354
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=35, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=35, out_features=4, bias=True)
)
No. of inputs : 3
No. of nodes : 35
No. of parameters : 284
Voting weight:  [1.0]
100% (280 of 280) |######################| Elapsed Time: 0:02:58 ETA:  00:00:00

=== Performance result ===
Accuracy:  97.2874551971326 (+/-) 8.887643166265176
Testing Loss:  0.16569871156339577 (+/-) 0.2294121423009497
Precision:  0.9728148130082398
Recall:  0.9728745519713262
F1 score:  0.9728033593001371
Testing Time:  0.00688018679191562 (+/-) 0.006612166765035878
Training Time:  0.6289730687295237 (+/-) 0.014261620071242511


=== Average network evolution ===
Total hidden node:  28.781362007168457 (+/-) 8.871375393406758
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=39, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=39, out_features=4, bias=True)
)
No. of inputs : 3
No. of nodes : 39
No. of parameters : 316
Voting weight:  [1.0]
100% (280 of 280) |######################| Elapsed Time: 0:02:57 ETA:  00:00:00

=== Performance result ===
Accuracy:  96.6247311827957 (+/-) 10.543502535508198
Testing Loss:  0.18553328344524975 (+/-) 0.26440062345039544
Precision:  0.9663343437674362
Recall:  0.966247311827957
F1 score:  0.9661035537325324
Testing Time:  0.006583026660385952 (+/-) 0.006271520355453724
Training Time:  0.6265867328985617 (+/-) 0.015350101266807947


=== Average network evolution ===
Total hidden node:  24.483870967741936 (+/-) 9.349003247304907
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=35, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=35, out_features=4, bias=True)
)
No. of inputs : 3
No. of nodes : 35
No. of parameters : 284
Voting weight:  [1.0]
100% (280 of 280) |######################| Elapsed Time: 0:02:58 ETA:  00:00:00

=== Performance result ===
Accuracy:  96.97634408602151 (+/-) 9.947850377648685
Testing Loss:  0.1638026074656556 (+/-) 0.24087922789386126
Precision:  0.9697826733550973
Recall:  0.969763440860215
F1 score:  0.9696495855052154
Testing Time:  0.0071347033251143695 (+/-) 0.007121345875750086
Training Time:  0.6299593089729227 (+/-) 0.021357610810543378


=== Average network evolution ===
Total hidden node:  27.634408602150536 (+/-) 9.24162115474397
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=38, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=38, out_features=4, bias=True)
)
No. of inputs : 3
No. of nodes : 38
No. of parameters : 308
Voting weight:  [1.0]
100% (280 of 280) |######################| Elapsed Time: 0:02:59 ETA:  00:00:00

=== Performance result ===
Accuracy:  96.86308243727599 (+/-) 10.70074692551008
Testing Loss:  0.16308473168762141 (+/-) 0.2508491187722539
Precision:  0.9686463793230913
Recall:  0.9686308243727598
F1 score:  0.9685916523727954
Testing Time:  0.007133259140889705 (+/-) 0.007310075773317006
Training Time:  0.6325048665419274 (+/-) 0.020206184373702977


=== Average network evolution ===
Total hidden node:  28.752688172043012 (+/-) 9.77487370026684
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=39, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=39, out_features=4, bias=True)
)
No. of inputs : 3
No. of nodes : 39
No. of parameters : 316
Voting weight:  [1.0]
Mean Accuracy:  97.07129496402877
Std Accuracy:  9.535294703660611
Hidden Node mean 27.062589928057555
Hidden Node std:  9.271971947534666
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
25% labeled
100% (280 of 280) |######################| Elapsed Time: 0:01:30 ETA:  00:00:00

=== Performance result ===
Accuracy:  94.85913978494624 (+/-) 12.84661848225006
Testing Loss:  0.30868953629313406 (+/-) 0.2988003275884461
Precision:  0.9485775169310373
Recall:  0.9485913978494623
F1 score:  0.9483909461485568
Testing Time:  0.006202632808343484 (+/-) 0.006954339840470981
Training Time:  0.3159602901841577 (+/-) 0.010865868483907633


=== Average network evolution ===
Total hidden node:  18.157706093189965 (+/-) 7.032709275657048
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=28, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=28, out_features=4, bias=True)
)
No. of inputs : 3
No. of nodes : 28
No. of parameters : 228
Voting weight:  [1.0]
100% (280 of 280) |######################| Elapsed Time: 0:01:30 ETA:  00:00:00

=== Performance result ===
Accuracy:  94.85698924731183 (+/-) 13.562295387151217
Testing Loss:  0.3015872121231103 (+/-) 0.30787954199654205
Precision:  0.9484695656524856
Recall:  0.9485698924731183
F1 score:  0.9484659510032419
Testing Time:  0.005951648971940454 (+/-) 0.006345126446401811
Training Time:  0.3165681020333348 (+/-) 0.012128572358553818


=== Average network evolution ===
Total hidden node:  16.978494623655912 (+/-) 7.258630005848145
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=27, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=27, out_features=4, bias=True)
)
No. of inputs : 3
No. of nodes : 27
No. of parameters : 220
Voting weight:  [1.0]
100% (280 of 280) |######################| Elapsed Time: 0:01:30 ETA:  00:00:00

=== Performance result ===
Accuracy:  94.43405017921147 (+/-) 13.952765494985412
Testing Loss:  0.3004848081685309 (+/-) 0.31466128756713013
Precision:  0.9442726012091563
Recall:  0.9443405017921147
F1 score:  0.944000619121992
Testing Time:  0.006218871762675624 (+/-) 0.006886791794787729
Training Time:  0.3158271979260188 (+/-) 0.01184076982518149


=== Average network evolution ===
Total hidden node:  18.68100358422939 (+/-) 7.627673000895444
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=29, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=29, out_features=4, bias=True)
)
No. of inputs : 3
No. of nodes : 29
No. of parameters : 236
Voting weight:  [1.0]
100% (280 of 280) |######################| Elapsed Time: 0:01:31 ETA:  00:00:00

=== Performance result ===
Accuracy:  94.12114695340502 (+/-) 15.229946593355777
Testing Loss:  0.3254809337918476 (+/-) 0.3278054731938486
Precision:  0.9411412968255319
Recall:  0.9412114695340502
F1 score:  0.9410556739100882
Testing Time:  0.006053769033014988 (+/-) 0.006684823832240594
Training Time:  0.31703068193141704 (+/-) 0.009610329104591812


=== Average network evolution ===
Total hidden node:  16.29749103942652 (+/-) 6.7739508025764525
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=26, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=26, out_features=4, bias=True)
)
No. of inputs : 3
No. of nodes : 26
No. of parameters : 212
Voting weight:  [1.0]
100% (280 of 280) |######################| Elapsed Time: 0:01:31 ETA:  00:00:00

=== Performance result ===
Accuracy:  94.5146953405018 (+/-) 13.423745844798646
Testing Loss:  0.29723196094822285 (+/-) 0.30807067811378264
Precision:  0.9450020458853189
Recall:  0.945146953405018
F1 score:  0.9449517327961431
Testing Time:  0.006530056717575237 (+/-) 0.006574902147571334
Training Time:  0.3188580184854487 (+/-) 0.011226789728393539


=== Average network evolution ===
Total hidden node:  20.365591397849464 (+/-) 7.196850584943727
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=30, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=30, out_features=4, bias=True)
)
No. of inputs : 3
No. of nodes : 30
No. of parameters : 244
Voting weight:  [1.0]
Mean Accuracy:  94.793309352518
Std Accuracy:  13.277727545043847
Hidden Node mean 18.13956834532374
Hidden Node std:  7.297822861560166
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
Infinite Delay
 98% (277 of 280) |##################### | Elapsed Time: 0:00:01 ETA:   0:00:00

=== Performance result ===
Accuracy:  42.40716845878137 (+/-) 2.095468453525845
Testing Loss:  1.3447480975086117 (+/-) 0.0032307752740695296
C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Precision:  0.30913628633426576
Recall:  0.42407168458781364
F1 score:  0.31673680952970273
Testing Time:  0.004364403345251596 (+/-) 0.006388092798664148
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  7.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=7, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=7, out_features=4, bias=True)
)
No. of inputs : 3
No. of nodes : 7
No. of parameters : 60
Voting weight:  [1.0]
 98% (277 of 280) |##################### | Elapsed Time: 0:00:01 ETA:   0:00:00

=== Performance result ===
Accuracy:  31.21111111111111 (+/-) 1.4205299071279713
Testing Loss:  1.353252227161093 (+/-) 0.004727415697957422
C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Precision:  0.20549872350585713
Recall:  0.3121111111111111
F1 score:  0.1951421626018636
Testing Time:  0.004389546678057708 (+/-) 0.006040680977662103
Training Time:  3.5754241396449373e-06 (+/-) 5.961423372302035e-05


=== Average network evolution ===
Total hidden node:  7.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=7, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=7, out_features=4, bias=True)
)
No. of inputs : 3
No. of nodes : 7
No. of parameters : 60
Voting weight:  [1.0]
 98% (277 of 280) |##################### | Elapsed Time: 0:00:01 ETA:   0:00:00

=== Performance result ===
Accuracy:  25.015412186379926 (+/-) 0.10060262148700942
Testing Loss:  1.417539927267259 (+/-) 0.0034650218389932915
C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Precision:  0.06257708468544854
Recall:  0.25015412186379926
F1 score:  0.10011099206257089
Testing Time:  0.0043865711458267705 (+/-) 0.006557407237715854
Training Time:  1.060834494970178e-05 (+/-) 0.00010176384164200318


=== Average network evolution ===
Total hidden node:  6.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=4, bias=True)
)
No. of inputs : 3
No. of nodes : 6
No. of parameters : 52
Voting weight:  [1.0]
100% (280 of 280) |######################| Elapsed Time: 0:00:01 ETA:  00:00:00

=== Performance result ===
Accuracy:  46.164516129032265 (+/-) 1.4130963354718897
Testing Loss:  1.258284276958862 (+/-) 0.0037313205827181005
C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Precision:  0.24384289115897775
Recall:  0.46164516129032257
F1 score:  0.3190244457410917
Testing Time:  0.004496834184106533 (+/-) 0.006526282092032412
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  8.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=8, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=8, out_features=4, bias=True)
)
No. of inputs : 3
No. of nodes : 8
No. of parameters : 68
Voting weight:  [1.0]
 98% (277 of 280) |##################### | Elapsed Time: 0:00:01 ETA:   0:00:00

=== Performance result ===
Accuracy:  45.4752688172043 (+/-) 1.2230453718079113
Testing Loss:  1.3584843331340393 (+/-) 0.003134580110440132
C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Precision:  0.32793047492962757
Recall:  0.454752688172043
F1 score:  0.344701343924272
Testing Time:  0.004483316107035538 (+/-) 0.006144646097160609
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  5.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=3, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=4, bias=True)
)
No. of inputs : 3
No. of nodes : 5
No. of parameters : 44
Voting weight:  [1.0]
Mean Accuracy:  38.05410071942446
Std Accuracy:  8.563474032228214
Hidden Node mean 6.6
Hidden Node std:  1.019803902718557
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
In [5]:
%run ADL_pmnist.ipynb
Number of input:  784
Number of output:  10
Number of batch:  69
All labeled
100% (69 of 69) |########################| Elapsed Time: 0:01:32 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.62941176470589 (+/-) 14.036245714991821
Testing Loss:  0.552230480400955 (+/-) 0.4601719522848934
Precision:  0.8366778012648964
Recall:  0.8362941176470589
F1 score:  0.836128807994261
Testing Time:  0.007289672599119299 (+/-) 0.007909643991656556
Training Time:  1.3507589662776274 (+/-) 0.02316126952822371


=== Average network evolution ===
Total hidden node:  18.0 (+/-) 2.1282414723677108
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=21, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=21, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 21
No. of parameters : 16705
Voting weight:  [1.0]
100% (69 of 69) |########################| Elapsed Time: 0:01:33 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.27352941176471 (+/-) 14.133716650859464
Testing Loss:  0.5500972243573736 (+/-) 0.42934932940038917
Precision:  0.8323229148390149
Recall:  0.832735294117647
F1 score:  0.8323599340561967
Testing Time:  0.0076811523998484895 (+/-) 0.008031525499286828
Training Time:  1.3629411634276896 (+/-) 0.026443727340209156


=== Average network evolution ===
Total hidden node:  22.485294117647058 (+/-) 5.598037069468356
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=32, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=32, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 32
No. of parameters : 25450
Voting weight:  [1.0]
100% (69 of 69) |########################| Elapsed Time: 0:01:33 ETA:  00:00:00

=== Performance result ===
Accuracy:  84.17352941176469 (+/-) 12.999735491054047
Testing Loss:  0.5271609309403336 (+/-) 0.3901938455706791
Precision:  0.842395720809963
Recall:  0.841735294117647
F1 score:  0.8416488865054185
Testing Time:  0.006790687056148753 (+/-) 0.005347995527207314
Training Time:  1.36243837370592 (+/-) 0.027787686681828197


=== Average network evolution ===
Total hidden node:  20.75 (+/-) 2.493373571028995
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=24, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=24, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 24
No. of parameters : 19090
Voting weight:  [1.0]
100% (69 of 69) |########################| Elapsed Time: 0:01:32 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.39264705882353 (+/-) 13.232450266368021
Testing Loss:  0.5556339027688784 (+/-) 0.42317462234461284
Precision:  0.8339532263070639
Recall:  0.8339264705882353
F1 score:  0.8336482969002523
Testing Time:  0.006170244777903837 (+/-) 0.00611739323925471
Training Time:  1.3465271206463085 (+/-) 0.021330209722165726


=== Average network evolution ===
Total hidden node:  16.720588235294116 (+/-) 1.7476504246903657
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=19, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=19, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 19
No. of parameters : 15115
Voting weight:  [1.0]
100% (69 of 69) |########################| Elapsed Time: 0:01:32 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.65294117647059 (+/-) 13.627065095396986
Testing Loss:  0.5313457902520895 (+/-) 0.4122060554597495
Precision:  0.836966400681923
Recall:  0.8365294117647059
F1 score:  0.836618124909882
Testing Time:  0.006714179235346177 (+/-) 0.005664544494135549
Training Time:  1.3590740491362179 (+/-) 0.023432576095128933


=== Average network evolution ===
Total hidden node:  23.264705882352942 (+/-) 4.8586419279550785
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=29, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=29, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 29
No. of parameters : 23065
Voting weight:  [1.0]
Mean Accuracy:  84.50268656716418
Std Accuracy:  11.649962857168328
Hidden Node mean 20.34328358208955
Hidden Node std:  4.4431810535701874
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
50% labeled
100% (69 of 69) |########################| Elapsed Time: 0:00:45 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.98970588235295 (+/-) 16.4116183711587
Testing Loss:  0.7134335104595212 (+/-) 0.4803386855977398
Precision:  0.7891597852797784
Recall:  0.7898970588235295
F1 score:  0.7890300314984406
Testing Time:  0.006696732605204862 (+/-) 0.0068850353178534635
Training Time:  0.6632042632383459 (+/-) 0.02017481504654941


=== Average network evolution ===
Total hidden node:  13.382352941176471 (+/-) 1.7491965530822653
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=16, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=16, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 16
No. of parameters : 12730
Voting weight:  [1.0]
100% (69 of 69) |########################| Elapsed Time: 0:00:45 ETA:  00:00:00

=== Performance result ===
Accuracy:  80.5029411764706 (+/-) 16.29932306956186
Testing Loss:  0.6610048402319936 (+/-) 0.49523816976448815
Precision:  0.8047729169287726
Recall:  0.8050294117647059
F1 score:  0.8043986263177768
Testing Time:  0.006828732350293328 (+/-) 0.007900593862392809
Training Time:  0.6664299228612114 (+/-) 0.015689287860185374


=== Average network evolution ===
Total hidden node:  19.220588235294116 (+/-) 2.909647180123866
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=22, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=22, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 22
No. of parameters : 17500
Voting weight:  [1.0]
100% (69 of 69) |########################| Elapsed Time: 0:00:45 ETA:  00:00:00

=== Performance result ===
Accuracy:  80.63970588235294 (+/-) 15.39208295067804
Testing Loss:  0.6500660115305115 (+/-) 0.44443011877182
Precision:  0.8075020667601315
Recall:  0.8063970588235294
F1 score:  0.8063987794105941
Testing Time:  0.006120822008918314 (+/-) 0.005663973018455904
Training Time:  0.6647178635877722 (+/-) 0.014765264387780773


=== Average network evolution ===
Total hidden node:  18.147058823529413 (+/-) 1.8492025776021352
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=21, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=21, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 21
No. of parameters : 16705
Voting weight:  [1.0]
100% (69 of 69) |########################| Elapsed Time: 0:00:45 ETA:  00:00:00

=== Performance result ===
Accuracy:  81.02794117647059 (+/-) 14.645756719230754
Testing Loss:  0.6483502214884057 (+/-) 0.4476033750598016
Precision:  0.8096041032492314
Recall:  0.8102794117647059
F1 score:  0.8095383991546553
Testing Time:  0.00608450174331665 (+/-) 0.005764129174285193
Training Time:  0.6617630860384773 (+/-) 0.016135120444542086


=== Average network evolution ===
Total hidden node:  17.102941176470587 (+/-) 1.6639756244887525
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=19, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=19, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 19
No. of parameters : 15115
Voting weight:  [1.0]
100% (69 of 69) |########################| Elapsed Time: 0:00:45 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.54411764705884 (+/-) 16.79638776250751
Testing Loss:  0.728066055871108 (+/-) 0.4825004176641091
Precision:  0.7863269703522083
Recall:  0.7854411764705882
F1 score:  0.7851103320811684
Testing Time:  0.005749232628766228 (+/-) 0.005676878708404256
Training Time:  0.6561268357669606 (+/-) 0.013449282345160624


=== Average network evolution ===
Total hidden node:  12.147058823529411 (+/-) 0.9589317874647709
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=14, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=14, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 14
No. of parameters : 11140
Voting weight:  [1.0]
Mean Accuracy:  80.83462686567165
Std Accuracy:  14.283703095293674
Hidden Node mean 16.04179104477612
Hidden Node std:  3.365059678951457
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
25% labeled
100% (69 of 69) |########################| Elapsed Time: 0:00:23 ETA:  00:00:00

=== Performance result ===
Accuracy:  75.09705882352942 (+/-) 19.12392996305503
Testing Loss:  0.8722659605829155 (+/-) 0.5292670950656785
Precision:  0.7516246374707763
Recall:  0.7509705882352942
F1 score:  0.7503042395694804
Testing Time:  0.006519692785599653 (+/-) 0.0081515543101675
Training Time:  0.333015361252953 (+/-) 0.009836519703710005


=== Average network evolution ===
Total hidden node:  14.411764705882353 (+/-) 1.1786744064419927
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=15, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=15, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 15
No. of parameters : 11935
Voting weight:  [1.0]
100% (69 of 69) |########################| Elapsed Time: 0:00:23 ETA:  00:00:00

=== Performance result ===
Accuracy:  75.64117647058823 (+/-) 18.621123641572773
Testing Loss:  0.8683138732962749 (+/-) 0.5201541385011949
Precision:  0.755873514456058
Recall:  0.7564117647058823
F1 score:  0.7543382285876298
Testing Time:  0.0066058811019448676 (+/-) 0.007617518638133852
Training Time:  0.33164596908232746 (+/-) 0.010653390946010421


=== Average network evolution ===
Total hidden node:  14.088235294117647 (+/-) 1.8844198211342762
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=16, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=16, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 16
No. of parameters : 12730
Voting weight:  [1.0]
100% (69 of 69) |########################| Elapsed Time: 0:00:23 ETA:  00:00:00

=== Performance result ===
Accuracy:  73.54705882352941 (+/-) 18.341262309726307
Testing Loss:  0.9202936703667921 (+/-) 0.5331370084082283
Precision:  0.7353030837489376
Recall:  0.7354705882352941
F1 score:  0.7321224642450781
Testing Time:  0.005626128000371596 (+/-) 0.00582289670965057
Training Time:  0.33117961182313804 (+/-) 0.011051418493841867


=== Average network evolution ===
Total hidden node:  12.720588235294118 (+/-) 0.8718740504577435
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=14, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=14, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 14
No. of parameters : 11140
Voting weight:  [1.0]
100% (69 of 69) |########################| Elapsed Time: 0:00:23 ETA:  00:00:00

=== Performance result ===
Accuracy:  75.87205882352939 (+/-) 18.39113313839544
Testing Loss:  0.8563937233651385 (+/-) 0.518690581676897
Precision:  0.7589136305240193
Recall:  0.7587205882352941
F1 score:  0.7571520016731739
Testing Time:  0.005658226854660932 (+/-) 0.0059441272209432485
Training Time:  0.33192208584617167 (+/-) 0.011045585181986381


=== Average network evolution ===
Total hidden node:  13.838235294117647 (+/-) 1.1062694879326629
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=15, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=15, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 15
No. of parameters : 11935
Voting weight:  [1.0]
100% (69 of 69) |########################| Elapsed Time: 0:00:23 ETA:  00:00:00

=== Performance result ===
Accuracy:  75.95 (+/-) 18.016744662543623
Testing Loss:  0.8309477845973828 (+/-) 0.5059725847734725
Precision:  0.7593572020148537
Recall:  0.7595
F1 score:  0.7573258769021564
Testing Time:  0.005799977218403536 (+/-) 0.005575867648111063
Training Time:  0.3371916027630077 (+/-) 0.012132168664260591


=== Average network evolution ===
Total hidden node:  15.867647058823529 (+/-) 1.4235871402976217
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=18, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=18, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 18
No. of parameters : 14320
Voting weight:  [1.0]
Mean Accuracy:  76.10955223880599
Std Accuracy:  17.160766554986225
Hidden Node mean 14.211940298507463
Hidden Node std:  1.6750604701100447
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
Infinite Delay
 92% (64 of 69) |######################  | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  23.144117647058824 (+/-) 20.96783518765701
Testing Loss:  2.0989189428441666 (+/-) 0.3850720411510058
Precision:  0.5059563090839508
Recall:  0.23144117647058823
F1 score:  0.2283668225353558
Testing Time:  0.00488353827420403 (+/-) 0.006495506972959082
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  13.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=13, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=13, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 13
No. of parameters : 10345
Voting weight:  [1.0]
 98% (68 of 69) |####################### | Elapsed Time: 0:00:00 ETA:   0:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  20.539705882352937 (+/-) 19.843951069057336
Testing Loss:  2.156495941035888 (+/-) 0.37661902137790076
Precision:  0.502241499891289
Recall:  0.2053970588235294
F1 score:  0.19696785567150626
Testing Time:  0.005074059261995203 (+/-) 0.006220636236936215
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  13.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=13, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=13, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 13
No. of parameters : 10345
Voting weight:  [1.0]
 94% (65 of 69) |######################  | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  20.85147058823529 (+/-) 22.707361417462238
Testing Loss:  2.17113817264052 (+/-) 0.3787638877617473
Precision:  0.5208692707505422
Recall:  0.20851470588235294
F1 score:  0.21451192981234068
Testing Time:  0.004898828618666705 (+/-) 0.005936488267147047
Training Time:  1.4655730303596047e-05 (+/-) 0.00011996232266435502


=== Average network evolution ===
Total hidden node:  13.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=13, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=13, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 13
No. of parameters : 10345
Voting weight:  [1.0]
 88% (61 of 69) |#####################   | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  25.68088235294118 (+/-) 23.58522255037813
Testing Loss:  2.0663314812323628 (+/-) 0.37271079248281036
Precision:  0.4596745345436153
Recall:  0.25680882352941176
F1 score:  0.2675956740302738
Testing Time:  0.004575287594514734 (+/-) 0.005590206552787734
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  13.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=13, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=13, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 13
No. of parameters : 10345
Voting weight:  [1.0]
 91% (63 of 69) |#####################   | Elapsed Time: 0:00:00 ETA:   0:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  23.110294117647054 (+/-) 24.08607200477855
Testing Loss:  2.099849259152132 (+/-) 0.4145151207936271
Precision:  0.413975226061566
Recall:  0.2311029411764706
F1 score:  0.22408442935146733
Testing Time:  0.004487367237315458 (+/-) 0.004284739165886483
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  13.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=13, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=13, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 13
No. of parameters : 10345
Voting weight:  [1.0]
Mean Accuracy:  22.663582089552236
Std Accuracy:  22.538950412033323
Hidden Node mean 13.0
Hidden Node std:  0.0
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
In [6]:
%run ADL_rmnist.ipynb
Number of input:  784
Number of output:  10
Number of batch:  69
All labeled
100% (69 of 69) |########################| Elapsed Time: 0:01:33 ETA:  00:00:00

=== Performance result ===
Accuracy:  89.56911764705883 (+/-) 4.0122700539917755
Testing Loss:  0.37158865437788124 (+/-) 0.18384539539433573
Precision:  0.8953301748883141
Recall:  0.8956911764705883
F1 score:  0.8952943915392418
Testing Time:  0.007246508317835191 (+/-) 0.007861702456245944
Training Time:  1.3589341956026413 (+/-) 0.024262420094089545


=== Average network evolution ===
Total hidden node:  21.794117647058822 (+/-) 4.333721480265213
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=29, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=29, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 29
No. of parameters : 23065
Voting weight:  [1.0]
100% (69 of 69) |########################| Elapsed Time: 0:01:32 ETA:  00:00:00

=== Performance result ===
Accuracy:  88.94558823529411 (+/-) 4.795783498017982
Testing Loss:  0.39259583492051153 (+/-) 0.1960947791522328
Precision:  0.8893137288221734
Recall:  0.8894558823529412
F1 score:  0.8892496341085132
Testing Time:  0.006432056427001953 (+/-) 0.006339450986640934
Training Time:  1.358168766779058 (+/-) 0.02588178101706694


=== Average network evolution ===
Total hidden node:  20.941176470588236 (+/-) 4.850355817576007
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=29, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=29, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 29
No. of parameters : 23065
Voting weight:  [1.0]
100% (69 of 69) |########################| Elapsed Time: 0:01:33 ETA:  00:00:00

=== Performance result ===
Accuracy:  89.81764705882352 (+/-) 4.651911092939009
Testing Loss:  0.36535090525798936 (+/-) 0.19268950119153092
Precision:  0.8980069174825702
Recall:  0.8981764705882352
F1 score:  0.8979554496803271
Testing Time:  0.006704547825981589 (+/-) 0.005028431103552435
Training Time:  1.360401707537034 (+/-) 0.03103397707023442


=== Average network evolution ===
Total hidden node:  22.352941176470587 (+/-) 4.831772010478731
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=30, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=30, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 30
No. of parameters : 23860
Voting weight:  [1.0]
100% (69 of 69) |########################| Elapsed Time: 0:01:32 ETA:  00:00:00

=== Performance result ===
Accuracy:  89.36470588235294 (+/-) 3.9529127274886635
Testing Loss:  0.38666333181454854 (+/-) 0.19428607323949487
Precision:  0.8933551290104585
Recall:  0.8936470588235295
F1 score:  0.8932763253887217
Testing Time:  0.006505675175610711 (+/-) 0.005244149723956401
Training Time:  1.3457404164706959 (+/-) 0.019674688004044573


=== Average network evolution ===
Total hidden node:  19.147058823529413 (+/-) 2.906709813124843
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=23, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=23, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 23
No. of parameters : 18295
Voting weight:  [1.0]
100% (69 of 69) |########################| Elapsed Time: 0:01:31 ETA:  00:00:00

=== Performance result ===
Accuracy:  89.83088235294117 (+/-) 3.926203725234023
Testing Loss:  0.3655843844308573 (+/-) 0.1779091644879075
Precision:  0.8980480452505217
Recall:  0.8983088235294118
F1 score:  0.8979916326669882
Testing Time:  0.006541553665609921 (+/-) 0.0052393677052419265
Training Time:  1.342955298283521 (+/-) 0.03405930998993153


=== Average network evolution ===
Total hidden node:  21.044117647058822 (+/-) 2.783532581669917
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=25, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=25, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 25
No. of parameters : 19885
Voting weight:  [1.0]
Mean Accuracy:  89.79373134328358
Std Accuracy:  3.5917055507995728
Hidden Node mean 21.170149253731342
Hidden Node std:  4.111614961260797
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
50% labeled
100% (69 of 69) |########################| Elapsed Time: 0:00:46 ETA:  00:00:00

=== Performance result ===
Accuracy:  86.98382352941178 (+/-) 6.129163897833472
Testing Loss:  0.4804294142214691 (+/-) 0.27243974647002756
Precision:  0.8692266858417849
Recall:  0.8698382352941176
F1 score:  0.869159186963459
Testing Time:  0.006842648281770594 (+/-) 0.008092308218929922
Training Time:  0.6680060134214514 (+/-) 0.01821675302479644


=== Average network evolution ===
Total hidden node:  16.764705882352942 (+/-) 1.70740297337647
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=19, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=19, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 19
No. of parameters : 15115
Voting weight:  [1.0]
100% (69 of 69) |########################| Elapsed Time: 0:00:45 ETA:  00:00:00

=== Performance result ===
Accuracy:  87.0235294117647 (+/-) 6.4635657079432285
Testing Loss:  0.46894242329632535 (+/-) 0.2540721369574389
Precision:  0.8701184410601098
Recall:  0.8702352941176471
F1 score:  0.8696154279445064
Testing Time:  0.006566587616415585 (+/-) 0.006823347317057662
Training Time:  0.6680324638591093 (+/-) 0.015447335120051555


=== Average network evolution ===
Total hidden node:  17.08823529411765 (+/-) 1.4114583000825676
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=19, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=19, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 19
No. of parameters : 15115
Voting weight:  [1.0]
100% (69 of 69) |########################| Elapsed Time: 0:00:46 ETA:  00:00:00

=== Performance result ===
Accuracy:  87.83529411764705 (+/-) 5.575175141165637
Testing Loss:  0.44894385348786325 (+/-) 0.2416725329025003
Precision:  0.8779035411631845
Recall:  0.8783529411764706
F1 score:  0.8779205800107512
Testing Time:  0.006344065946691176 (+/-) 0.0056232377576093
Training Time:  0.6746709977879244 (+/-) 0.015139351175214637


=== Average network evolution ===
Total hidden node:  19.455882352941178 (+/-) 2.69767777874302
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=25, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=25, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 25
No. of parameters : 19885
Voting weight:  [1.0]
100% (69 of 69) |########################| Elapsed Time: 0:00:45 ETA:  00:00:00

=== Performance result ===
Accuracy:  87.28529411764706 (+/-) 6.059321081797724
Testing Loss:  0.4811044517247116 (+/-) 0.25507087215849683
Precision:  0.8725587053236206
Recall:  0.8728529411764706
F1 score:  0.872300683123871
Testing Time:  0.006016629583695356 (+/-) 0.005670217456542084
Training Time:  0.6663284722496482 (+/-) 0.016990761572798496


=== Average network evolution ===
Total hidden node:  17.58823529411765 (+/-) 2.5852255227465566
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=21, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=21, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 21
No. of parameters : 16705
Voting weight:  [1.0]
100% (69 of 69) |########################| Elapsed Time: 0:00:46 ETA:  00:00:00

=== Performance result ===
Accuracy:  88.11617647058824 (+/-) 5.42631416162231
Testing Loss:  0.4447358674643671 (+/-) 0.2541047302337785
Precision:  0.8805529631965382
Recall:  0.8811617647058824
F1 score:  0.8806431869139512
Testing Time:  0.006567404550664565 (+/-) 0.0060915653297951835
Training Time:  0.6716056746595046 (+/-) 0.017708610781394855


=== Average network evolution ===
Total hidden node:  23.08823529411765 (+/-) 3.890921915999007
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=30, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=30, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 30
No. of parameters : 23860
Voting weight:  [1.0]
Mean Accuracy:  87.9002985074627
Std Accuracy:  4.691424367658554
Hidden Node mean 18.86268656716418
Hidden Node std:  3.4854424683628507
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
25% labeled
100% (69 of 69) |########################| Elapsed Time: 0:00:23 ETA:  00:00:00

=== Performance result ===
Accuracy:  84.00588235294117 (+/-) 9.08226912487994
Testing Loss:  0.6258231723571525 (+/-) 0.36077197450154447
Precision:  0.8391205759758197
Recall:  0.8400588235294117
F1 score:  0.838949776687193
Testing Time:  0.006316497045404771 (+/-) 0.00800372580737738
Training Time:  0.335057605715359 (+/-) 0.011112340488730353


=== Average network evolution ===
Total hidden node:  14.088235294117647 (+/-) 1.4924840536900323
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=16, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=16, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 16
No. of parameters : 12730
Voting weight:  [1.0]
100% (69 of 69) |########################| Elapsed Time: 0:00:23 ETA:  00:00:00

=== Performance result ===
Accuracy:  85.18970588235295 (+/-) 7.771557109968041
Testing Loss:  0.5779462419450283 (+/-) 0.3298802443682519
Precision:  0.8517053262382698
Recall:  0.8518970588235294
F1 score:  0.8507589752007055
Testing Time:  0.0070640269447775446 (+/-) 0.007891563918854795
Training Time:  0.3394626729628619 (+/-) 0.010723921175889965


=== Average network evolution ===
Total hidden node:  17.058823529411764 (+/-) 1.7563527796274843
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=20, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=20, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 20
No. of parameters : 15910
Voting weight:  [1.0]
100% (69 of 69) |########################| Elapsed Time: 0:00:23 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.65588235294116 (+/-) 9.04751155831944
Testing Loss:  0.6313754193046514 (+/-) 0.35493663555211563
Precision:  0.8357763123128847
Recall:  0.8365588235294118
F1 score:  0.8352559594296709
Testing Time:  0.006006093586192411 (+/-) 0.005781876503660694
Training Time:  0.3373185431256014 (+/-) 0.01201437388570467


=== Average network evolution ===
Total hidden node:  15.308823529411764 (+/-) 2.0238094026537916
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=19, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=19, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 19
No. of parameters : 15115
Voting weight:  [1.0]
100% (69 of 69) |########################| Elapsed Time: 0:00:23 ETA:  00:00:00

=== Performance result ===
Accuracy:  84.43529411764705 (+/-) 9.14695006555355
Testing Loss:  0.6096363216638565 (+/-) 0.34431017979233014
Precision:  0.8439272409349576
Recall:  0.8443529411764706
F1 score:  0.8431941179138146
Testing Time:  0.00616295197430779 (+/-) 0.005875579077147709
Training Time:  0.3382856845855713 (+/-) 0.010722130464719688


=== Average network evolution ===
Total hidden node:  15.852941176470589 (+/-) 1.3854783192133975
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=18, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=18, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 18
No. of parameters : 14320
Voting weight:  [1.0]
100% (69 of 69) |########################| Elapsed Time: 0:00:23 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.33382352941176 (+/-) 9.673061597795213
Testing Loss:  0.6446357849327957 (+/-) 0.3501172821665198
Precision:  0.8331665191731664
Recall:  0.8333382352941177
F1 score:  0.8320004232468529
Testing Time:  0.005803862038780661 (+/-) 0.004716646188223038
Training Time:  0.33922698217279773 (+/-) 0.011489905682218417


=== Average network evolution ===
Total hidden node:  14.088235294117647 (+/-) 2.084503708162343
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=18, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=18, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 18
No. of parameters : 14320
Voting weight:  [1.0]
Mean Accuracy:  84.76835820895523
Std Accuracy:  7.328900998228562
Hidden Node mean 15.304477611940298
Hidden Node std:  2.098288772282508
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
Infinite Delay
 94% (65 of 69) |######################  | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  61.94558823529411 (+/-) 3.3903163926416275
Testing Loss:  1.556813490741393 (+/-) 0.059817927377113965
Precision:  0.6857822839618638
Recall:  0.6194558823529411
F1 score:  0.5920594705056055
Testing Time:  0.005322842036976534 (+/-) 0.007216117088999928
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  12.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=12, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=12, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 12
No. of parameters : 9550
Voting weight:  [1.0]
 97% (67 of 69) |####################### | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  58.622058823529414 (+/-) 3.8926342807408214
Testing Loss:  1.5050830104771782 (+/-) 0.06633649319569002
Precision:  0.703848217816864
Recall:  0.5862205882352941
F1 score:  0.5806317234937073
Testing Time:  0.005249759730170755 (+/-) 0.005997737489061065
Training Time:  1.4666248770321117e-05 (+/-) 0.00012004842002511888


=== Average network evolution ===
Total hidden node:  13.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=13, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=13, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 13
No. of parameters : 10345
Voting weight:  [1.0]
 94% (65 of 69) |######################  | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  59.17500000000001 (+/-) 3.3731367405707458
Testing Loss:  1.5079949445584242 (+/-) 0.07260987476581167
Precision:  0.6211730103197828
Recall:  0.59175
F1 score:  0.5418073303087408
Testing Time:  0.004986166954040527 (+/-) 0.005676172448656558
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  13.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=13, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=13, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 13
No. of parameters : 10345
Voting weight:  [1.0]
 98% (68 of 69) |####################### | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  61.8985294117647 (+/-) 5.01226415455304
Testing Loss:  1.4485924717258005 (+/-) 0.07445124422711086
Precision:  0.7156493084169475
Recall:  0.618985294117647
F1 score:  0.6108228086608515
Testing Time:  0.005147741121404311 (+/-) 0.006149533714278105
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  14.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=14, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=14, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 14
No. of parameters : 11140
Voting weight:  [1.0]
 89% (62 of 69) |#####################   | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  62.98529411764707 (+/-) 3.392347127158697
Testing Loss:  1.4689970244379604 (+/-) 0.06639035830425101
Precision:  0.6692403696501663
Recall:  0.6298529411764706
F1 score:  0.6107525499796178
Testing Time:  0.0045760729733635395 (+/-) 0.005382071267345738
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  14.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=784, out_features=14, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=14, out_features=10, bias=True)
)
No. of inputs : 784
No. of nodes : 14
No. of parameters : 11140
Voting weight:  [1.0]
Mean Accuracy:  60.84507462686568
Std Accuracy:  4.195131995196027
Hidden Node mean 13.2
Hidden Node std:  0.7483314773547882
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
In [7]:
%run ADL_hepmass.ipynb
Number of input:  28
Number of output:  2
Number of batch:  2000
All labeled
100% (2000 of 2000) |####################| Elapsed Time: 0:42:38 ETA:  00:00:00

=== Performance result ===
Accuracy:  84.13816908454228 (+/-) 1.6914548055163237
Testing Loss:  0.33302605788728007 (+/-) 0.027027352595151925
Precision:  0.8429299153266558
Recall:  0.8413816908454227
F1 score:  0.8412013572268461
Testing Time:  0.012572725514521177 (+/-) 0.009920815689287482
Training Time:  1.2501563833855938 (+/-) 0.02631145327018363


=== Average network evolution ===
Total hidden node:  4.943971985992997 (+/-) 0.32697995222977794
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=28, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 28
No. of nodes : 5
No. of parameters : 157
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:42:38 ETA:  00:00:00

=== Performance result ===
Accuracy:  84.13106553276639 (+/-) 1.6682991417071202
Testing Loss:  0.3331058333372104 (+/-) 0.027592446712496454
Precision:  0.8429554409309027
Recall:  0.8413106553276638
F1 score:  0.8411190244126483
Testing Time:  0.012605977094191322 (+/-) 0.010040343349018072
Training Time:  1.2497497101078157 (+/-) 0.02490994663497221


=== Average network evolution ===
Total hidden node:  4.9289644822411205 (+/-) 0.37686534993961973
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=28, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 28
No. of nodes : 5
No. of parameters : 157
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:42:40 ETA:  00:00:00

=== Performance result ===
Accuracy:  84.15657828914458 (+/-) 1.639649268169928
Testing Loss:  0.33212350627194054 (+/-) 0.0254141726857162
Precision:  0.8432917138778665
Recall:  0.8415657828914457
F1 score:  0.8413652346237308
Testing Time:  0.012782962874450226 (+/-) 0.010160404472267903
Training Time:  1.250524992463349 (+/-) 0.023092571666422177


=== Average network evolution ===
Total hidden node:  4.955977988994497 (+/-) 0.5515570134494715
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=28, out_features=4, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 28
No. of nodes : 4
No. of parameters : 126
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:42:48 ETA:  00:00:00

=== Performance result ===
Accuracy:  84.41970985492746 (+/-) 1.595131323066174
Testing Loss:  0.3284256686235202 (+/-) 0.025356202495859157
Precision:  0.8459054333217425
Recall:  0.8441970985492746
F1 score:  0.8440033660752372
Testing Time:  0.012899168256880821 (+/-) 0.010035063367205081
Training Time:  1.2546261278851858 (+/-) 0.028673484416009688


=== Average network evolution ===
Total hidden node:  6.136068034017009 (+/-) 0.3627127343194688
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=28, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 28
No. of nodes : 6
No. of parameters : 188
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:43:01 ETA:  00:00:00

=== Performance result ===
Accuracy:  84.15092546273137 (+/-) 1.4998141935768579
Testing Loss:  0.33179149991753937 (+/-) 0.02435458658050768
Precision:  0.8429556284650371
Recall:  0.8415092546273136
F1 score:  0.8413409093430227
Testing Time:  0.012693894631031336 (+/-) 0.009880717628207834
Training Time:  1.2614545855538852 (+/-) 0.025257858768041427


=== Average network evolution ===
Total hidden node:  5.986493246623311 (+/-) 0.13902274091190991
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=28, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 28
No. of nodes : 6
No. of parameters : 188
Voting weight:  [1.0]
Mean Accuracy:  84.21250250250249
Std Accuracy:  1.511312313405934
Hidden Node mean 5.391191191191191
Hidden Node std:  0.6643522005147656
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
50% labeled
100% (2000 of 2000) |####################| Elapsed Time: 0:22:01 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.52741370685342 (+/-) 2.2991828951662217
Testing Loss:  0.3406173401084049 (+/-) 0.03376605071207909
Precision:  0.8370602682742696
Recall:  0.8352741370685343
F1 score:  0.835054366979639
Testing Time:  0.012215889114448581 (+/-) 0.009667828989623569
Training Time:  0.6316477155136788 (+/-) 0.016212408516869867


=== Average network evolution ===
Total hidden node:  4.630815407703852 (+/-) 0.5999590493301672
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=28, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 28
No. of nodes : 5
No. of parameters : 157
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:22:01 ETA:  00:00:00

=== Performance result ===
Accuracy:  84.07988994497249 (+/-) 1.9425041696527978
Testing Loss:  0.33042013370853596 (+/-) 0.030668149887326093
Precision:  0.8421684676050889
Recall:  0.8407988994497249
F1 score:  0.8406383911720591
Testing Time:  0.01265965800931777 (+/-) 0.009869017161973722
Training Time:  0.6311214277898151 (+/-) 0.01883264589830312


=== Average network evolution ===
Total hidden node:  7.685342671335667 (+/-) 0.4915903789593631
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=28, out_features=8, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 28
No. of nodes : 8
No. of parameters : 250
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:21:59 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.9047523761881 (+/-) 1.8381163625010668
Testing Loss:  0.3364269971370458 (+/-) 0.028807834017877853
Precision:  0.8402684432728482
Recall:  0.839047523761881
F1 score:  0.8389020132857483
Testing Time:  0.012524122712372422 (+/-) 0.00952241768027817
Training Time:  0.6303243031198827 (+/-) 0.016214143355380194


=== Average network evolution ===
Total hidden node:  6.016008004002001 (+/-) 0.28772428567684744
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=28, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 28
No. of nodes : 6
No. of parameters : 188
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:22:02 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.83226613306654 (+/-) 2.1962459255036197
Testing Loss:  0.33874515378815107 (+/-) 0.03485835448217538
Precision:  0.8400007295917159
Recall:  0.8383226613306654
F1 score:  0.8381217443025815
Testing Time:  0.012393322510979306 (+/-) 0.009869821647704436
Training Time:  0.6319127311821041 (+/-) 0.015577827714429233


=== Average network evolution ===
Total hidden node:  4.0945472736368185 (+/-) 0.3995561352224711
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=28, out_features=4, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 28
No. of nodes : 4
No. of parameters : 126
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:22:00 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.86188094047023 (+/-) 1.843601664509068
Testing Loss:  0.33629855118494384 (+/-) 0.028433472825252906
Precision:  0.8400100018947673
Recall:  0.8386188094047023
F1 score:  0.8384524890107581
Testing Time:  0.012502314508885608 (+/-) 0.009890909044901853
Training Time:  0.6310744385769392 (+/-) 0.016823065539712332


=== Average network evolution ===
Total hidden node:  5.290145072536268 (+/-) 0.4690073697938474
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=28, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 28
No. of nodes : 5
No. of parameters : 157
Voting weight:  [1.0]
Mean Accuracy:  83.85588588588588
Std Accuracy:  1.9318693045858437
Hidden Node mean 5.544044044044044
Hidden Node std:  1.3316100207303079
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
25% labeled
100% (2000 of 2000) |####################| Elapsed Time: 0:11:37 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.3568284142071 (+/-) 2.564675598693976
Testing Loss:  0.3452682128812505 (+/-) 0.03842571910049675
Precision:  0.8344120475294611
Recall:  0.8335682841420711
F1 score:  0.8334623579425567
Testing Time:  0.012672002939297712 (+/-) 0.010011346109301149
Training Time:  0.3188720054779129 (+/-) 0.011814273829837341


=== Average network evolution ===
Total hidden node:  6.043521760880441 (+/-) 0.5270158216219137
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=28, out_features=7, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 28
No. of nodes : 7
No. of parameters : 219
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:11:34 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.45947973986993 (+/-) 2.058268000782759
Testing Loss:  0.34304751650341275 (+/-) 0.03419052307359971
Precision:  0.8360104606664316
Recall:  0.8345947973986994
F1 score:  0.8344192697590035
Testing Time:  0.012422821293955388 (+/-) 0.009739921809315082
Training Time:  0.3177014010259066 (+/-) 0.011728058077953294


=== Average network evolution ===
Total hidden node:  6.057028514257128 (+/-) 0.37791637313019877
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=28, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 28
No. of nodes : 6
No. of parameters : 188
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:11:33 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.47888944472236 (+/-) 1.9273042879090831
Testing Loss:  0.34332850324982345 (+/-) 0.033716106381026636
Precision:  0.8361366096093388
Recall:  0.8347888944472236
F1 score:  0.83462203232571
Testing Time:  0.012395198849691876 (+/-) 0.009754258265551258
Training Time:  0.3173999484626575 (+/-) 0.010827725003493825


=== Average network evolution ===
Total hidden node:  5.451225612806403 (+/-) 0.6439756213745785
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=28, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 28
No. of nodes : 6
No. of parameters : 188
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:11:36 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.59564782391196 (+/-) 1.8260622442003076
Testing Loss:  0.34211599780596036 (+/-) 0.03224146237329054
Precision:  0.8371641530996696
Recall:  0.8359564782391196
F1 score:  0.8358084251905796
Testing Time:  0.012672097042478759 (+/-) 0.009787881220249775
Training Time:  0.3187490265747498 (+/-) 0.011403259355202117


=== Average network evolution ===
Total hidden node:  7.298149074537268 (+/-) 0.6887588621375267
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=28, out_features=8, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 28
No. of nodes : 8
No. of parameters : 250
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:11:32 ETA:  00:00:00

=== Performance result ===
Accuracy:  83.43611805902951 (+/-) 2.0220164226028614
Testing Loss:  0.34529156803249894 (+/-) 0.03487135493302157
Precision:  0.8357031982977841
Recall:  0.8343611805902952
F1 score:  0.8341943766696143
Testing Time:  0.012286856390345746 (+/-) 0.009762856871547617
Training Time:  0.31696684053983015 (+/-) 0.012024711638206051


=== Average network evolution ===
Total hidden node:  3.5802901450725364 (+/-) 0.7627073994308055
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=28, out_features=3, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 28
No. of nodes : 3
No. of parameters : 95
Voting weight:  [1.0]
Mean Accuracy:  83.48228228228228
Std Accuracy:  1.9513093051591788
Hidden Node mean 5.686686686686687
Hidden Node std:  1.359772743824416
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
Infinite Delay
100% (2000 of 2000) |####################| Elapsed Time: 0:00:54 ETA:  00:00:00

=== Performance result ===
Accuracy:  62.67798899449725 (+/-) 1.554684623973283
Testing Loss:  0.6374242153389564 (+/-) 0.005525817776874546
Precision:  0.7458737036753178
Recall:  0.6267798899449725
F1 score:  0.5753196222594495
Testing Time:  0.011182063337920486 (+/-) 0.00939307221528819
Training Time:  4.990211780695035e-07 (+/-) 2.2305744283736114e-05


=== Average network evolution ===
Total hidden node:  5.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=28, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 28
No. of nodes : 5
No. of parameters : 157
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:00:54 ETA:  00:00:00

=== Performance result ===
Accuracy:  56.17548774387194 (+/-) 1.5931096788825736
Testing Loss:  0.6556168000599096 (+/-) 0.006513087733077176
Precision:  0.7047826874158696
Recall:  0.5617548774387193
F1 score:  0.46897538865666893
Testing Time:  0.011050442447061238 (+/-) 0.009357753169711542
Training Time:  9.970882047469047e-07 (+/-) 3.150704426316052e-05


=== Average network evolution ===
Total hidden node:  4.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=28, out_features=4, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 28
No. of nodes : 4
No. of parameters : 126
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:00:54 ETA:  00:00:00

=== Performance result ===
Accuracy:  65.69854927463732 (+/-) 1.4930999866831898
Testing Loss:  0.6667438975687681 (+/-) 0.00221969986519877
Precision:  0.6630181515004179
Recall:  0.6569854927463732
F1 score:  0.6537710090092951
Testing Time:  0.011396741914772999 (+/-) 0.009379764271877703
Training Time:  4.986633712974652e-07 (+/-) 2.228975068123822e-05


=== Average network evolution ===
Total hidden node:  6.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=28, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 28
No. of nodes : 6
No. of parameters : 188
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:00:54 ETA:  00:00:00

=== Performance result ===
Accuracy:  53.372236118059035 (+/-) 1.6041699513744123
Testing Loss:  0.6844265985154938 (+/-) 0.00215083355586622
Precision:  0.6606683126636556
Recall:  0.5337223611805902
F1 score:  0.41884784834218325
Testing Time:  0.011092239764405824 (+/-) 0.009422762600552966
Training Time:  4.98782640221478e-07 (+/-) 2.229508188207085e-05


=== Average network evolution ===
Total hidden node:  4.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=28, out_features=4, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 28
No. of nodes : 4
No. of parameters : 126
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:00:54 ETA:  00:00:00

=== Performance result ===
Accuracy:  50.6855927963982 (+/-) 1.603015085690278
Testing Loss:  0.6937006667532165 (+/-) 0.002769225790506218
Precision:  0.5207578114713997
Recall:  0.506855927963982
F1 score:  0.40828839629165037
Testing Time:  0.010954023302048668 (+/-) 0.009276656812183365
Training Time:  2.493793932183377e-06 (+/-) 4.980101514031178e-05


=== Average network evolution ===
Total hidden node:  4.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=28, out_features=4, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 28
No. of nodes : 4
No. of parameters : 126
Voting weight:  [1.0]
Mean Accuracy:  57.72203203203203
Std Accuracy:  5.853774744020195
Hidden Node mean 4.6
Hidden Node std:  0.7999999999999999
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
In [8]:
%run ADL_susy.ipynb
Number of input:  18
Number of output:  2
Number of batch:  2000
All labeled
100% (2000 of 2000) |####################| Elapsed Time: 0:42:55 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.00530265132566 (+/-) 2.5477926759663605
Testing Loss:  0.46744417576207825 (+/-) 0.034255557328922166
Precision:  0.7822284144624688
Recall:  0.7800530265132566
F1 score:  0.7779677322139856
Testing Time:  0.013515181157396935 (+/-) 0.01008145384030837
Training Time:  1.2577700285746969 (+/-) 0.02352862081228845


=== Average network evolution ===
Total hidden node:  11.813906953476739 (+/-) 1.9604357544996043
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=18, out_features=13, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=13, out_features=2, bias=True)
)
No. of inputs : 18
No. of nodes : 13
No. of parameters : 275
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:42:42 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.05737868934467 (+/-) 2.672716977194962
Testing Loss:  0.46626426806564386 (+/-) 0.035204229603081366
Precision:  0.7825970670241548
Recall:  0.7805737868934467
F1 score:  0.7785662035835066
Testing Time:  0.014359636387865563 (+/-) 0.009999732500537532
Training Time:  1.2501758929191082 (+/-) 0.02367805899284882


=== Average network evolution ===
Total hidden node:  21.51225612806403 (+/-) 2.870512982869722
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=18, out_features=24, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=24, out_features=2, bias=True)
)
No. of inputs : 18
No. of nodes : 24
No. of parameters : 506
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:42:37 ETA:  00:00:00

=== Performance result ===
Accuracy:  77.87058529264631 (+/-) 2.867182515186555
Testing Loss:  0.4692309184066053 (+/-) 0.036828362257350214
Precision:  0.7807690216589541
Recall:  0.7787058529264632
F1 score:  0.7766410442188558
Testing Time:  0.013383330435321115 (+/-) 0.010024050849534581
Training Time:  1.2483128497098432 (+/-) 0.022542728630977116


=== Average network evolution ===
Total hidden node:  10.665332666333166 (+/-) 2.5922796403465656
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=18, out_features=13, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=13, out_features=2, bias=True)
)
No. of inputs : 18
No. of nodes : 13
No. of parameters : 275
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:42:33 ETA:  00:00:00

=== Performance result ===
Accuracy:  77.84502251125564 (+/-) 2.763022101352028
Testing Loss:  0.47003147539763285 (+/-) 0.0352543797824029
Precision:  0.7804174607789107
Recall:  0.7784502251125562
F1 score:  0.776423046312149
Testing Time:  0.013292218518889266 (+/-) 0.009987069496005542
Training Time:  1.2468084819081904 (+/-) 0.022622585632566555


=== Average network evolution ===
Total hidden node:  9.948474237118559 (+/-) 2.340637886574863
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=18, out_features=11, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=11, out_features=2, bias=True)
)
No. of inputs : 18
No. of nodes : 11
No. of parameters : 233
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:42:41 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.0760380190095 (+/-) 2.6494699386921328
Testing Loss:  0.4657830270216905 (+/-) 0.034402998354037456
Precision:  0.7827786394929879
Recall:  0.7807603801900951
F1 score:  0.7787589037511858
Testing Time:  0.014197529286608331 (+/-) 0.01040309580456962
Training Time:  1.2499625205755114 (+/-) 0.02104379380197161


=== Average network evolution ===
Total hidden node:  17.43271635817909 (+/-) 3.2872105274249397
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=18, out_features=20, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=20, out_features=2, bias=True)
)
No. of inputs : 18
No. of nodes : 20
No. of parameters : 422
Voting weight:  [1.0]
Mean Accuracy:  77.98137137137137
Std Accuracy:  2.6633034950466965
Hidden Node mean 14.277977977977978
Hidden Node std:  5.199032164832843
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
50% labeled
100% (2000 of 2000) |####################| Elapsed Time: 0:23:57 ETA:  00:00:00

=== Performance result ===
Accuracy:  76.76538269134566 (+/-) 3.7641063334296554
Testing Loss:  0.48676223821077064 (+/-) 0.045639399700106255
Precision:  0.7697171673423313
Recall:  0.7676538269134567
F1 score:  0.7653370657795279
Testing Time:  0.012887020716970118 (+/-) 0.010670375093770511
Training Time:  0.6879102717404845 (+/-) 0.0836781463421194


=== Average network evolution ===
Total hidden node:  8.652326163081542 (+/-) 2.9044623069196462
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=18, out_features=12, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=12, out_features=2, bias=True)
)
No. of inputs : 18
No. of nodes : 12
No. of parameters : 254
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:23:55 ETA:  00:00:00

=== Performance result ===
Accuracy:  77.15952976488242 (+/-) 3.3292070811056402
Testing Loss:  0.48024906716745097 (+/-) 0.04059501339373892
Precision:  0.7740878013303962
Recall:  0.7715952976488244
F1 score:  0.7691746374063072
Testing Time:  0.011883897862474938 (+/-) 0.009774998558244477
Training Time:  0.6894916786796871 (+/-) 0.07810592362584406


=== Average network evolution ===
Total hidden node:  9.481240620310155 (+/-) 1.8727725169066465
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=18, out_features=12, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=12, out_features=2, bias=True)
)
No. of inputs : 18
No. of nodes : 12
No. of parameters : 254
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:21:42 ETA:  00:00:00

=== Performance result ===
Accuracy:  77.06263131565784 (+/-) 3.2354510274945403
Testing Loss:  0.4820119719943027 (+/-) 0.041847091377951305
Precision:  0.7735225894718236
Recall:  0.7706263131565783
F1 score:  0.7680024828487249
Testing Time:  0.011178442333387459 (+/-) 0.008709680858879245
Training Time:  0.6242773209648648 (+/-) 0.014298204341692102


=== Average network evolution ===
Total hidden node:  10.174587293646823 (+/-) 1.9659519324640187
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=18, out_features=13, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=13, out_features=2, bias=True)
)
No. of inputs : 18
No. of nodes : 13
No. of parameters : 275
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:21:44 ETA:  00:00:00

=== Performance result ===
Accuracy:  77.18369184592297 (+/-) 3.1387354152734965
Testing Loss:  0.48067665602518 (+/-) 0.038927147076005336
Precision:  0.774053050889269
Recall:  0.7718369184592296
F1 score:  0.769547908084622
Testing Time:  0.01121166946292818 (+/-) 0.008992896111568751
Training Time:  0.6252112841832751 (+/-) 0.014864281890515868


=== Average network evolution ===
Total hidden node:  10.02751375687844 (+/-) 1.9497805501601284
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=18, out_features=12, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=12, out_features=2, bias=True)
)
No. of inputs : 18
No. of nodes : 12
No. of parameters : 254
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:23:31 ETA:  00:00:00

=== Performance result ===
Accuracy:  77.2176088044022 (+/-) 3.183987904268762
Testing Loss:  0.48033550973532496 (+/-) 0.041124696730940685
Precision:  0.7738405695960491
Recall:  0.772176088044022
F1 score:  0.7701582499174392
Testing Time:  0.012538481259596473 (+/-) 0.0100371965408198
Training Time:  0.676326369213545 (+/-) 0.04866747894628626


=== Average network evolution ===
Total hidden node:  13.534767383691847 (+/-) 3.1720179195991056
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=18, out_features=17, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=17, out_features=2, bias=True)
)
No. of inputs : 18
No. of nodes : 17
No. of parameters : 359
Voting weight:  [1.0]
Mean Accuracy:  77.08850850850851
Std Accuracy:  3.3081414536798803
Hidden Node mean 10.376376376376376
Hidden Node std:  2.9510322047642985
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
25% labeled
100% (2000 of 2000) |####################| Elapsed Time: 0:12:28 ETA:  00:00:00

=== Performance result ===
Accuracy:  75.10130065032516 (+/-) 5.294356751850267
Testing Loss:  0.5085699502499834 (+/-) 0.05720700284730417
Precision:  0.7540093993470135
Recall:  0.7510130065032516
F1 score:  0.7477619134411412
Testing Time:  0.011712452720081049 (+/-) 0.009883049287556995
Training Time:  0.34588963321115207 (+/-) 0.02586675338198594


=== Average network evolution ===
Total hidden node:  5.655827913956979 (+/-) 2.482072751748765
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=18, out_features=9, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 18
No. of nodes : 9
No. of parameters : 191
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:13:39 ETA:  00:00:00

=== Performance result ===
Accuracy:  75.80280140070036 (+/-) 4.31407902048346
Testing Loss:  0.49924272500079175 (+/-) 0.0502419985237688
Precision:  0.7607987265839188
Recall:  0.7580280140070035
F1 score:  0.7551089420243012
Testing Time:  0.01314068198382944 (+/-) 0.010657177879193675
Training Time:  0.37847294325587627 (+/-) 0.014704150658411318


=== Average network evolution ===
Total hidden node:  7.147073536768384 (+/-) 2.071358359343322
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=18, out_features=10, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=10, out_features=2, bias=True)
)
No. of inputs : 18
No. of nodes : 10
No. of parameters : 212
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:13:42 ETA:  00:00:00

=== Performance result ===
Accuracy:  74.736168084042 (+/-) 4.6052526206074385
Testing Loss:  0.515989195143598 (+/-) 0.05201111393000463
Precision:  0.7509023779895042
Recall:  0.7473616808404202
F1 score:  0.7437112976340987
Testing Time:  0.011321809901303801 (+/-) 0.009302504688498208
Training Time:  0.38179562615417967 (+/-) 0.010217282260838248


=== Average network evolution ===
Total hidden node:  2.7933966983491745 (+/-) 0.5069170550094307
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=18, out_features=3, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 18
No. of nodes : 3
No. of parameters : 65
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:13:45 ETA:  00:00:00

=== Performance result ===
Accuracy:  75.81770885442721 (+/-) 4.253482409694606
Testing Loss:  0.5000724779957232 (+/-) 0.04845101695909449
Precision:  0.7604699262560428
Recall:  0.7581770885442721
F1 score:  0.7554997650397659
Testing Time:  0.013061831986206422 (+/-) 0.010621359395612604
Training Time:  0.38141021769067057 (+/-) 0.010512446313708757


=== Average network evolution ===
Total hidden node:  6.164582291145573 (+/-) 2.087966957620405
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=18, out_features=9, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 18
No. of nodes : 9
No. of parameters : 191
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:13:39 ETA:  00:00:00

=== Performance result ===
Accuracy:  76.08409204602302 (+/-) 3.7165620511800825
Testing Loss:  0.49623057787390934 (+/-) 0.046188077879307524
Precision:  0.7635387885174375
Recall:  0.7608409204602301
F1 score:  0.7580390376670408
Testing Time:  0.013489275827832435 (+/-) 0.010735151251004094
Training Time:  0.37790738659658807 (+/-) 0.009398113618371205


=== Average network evolution ===
Total hidden node:  10.576788394197099 (+/-) 2.219784947546056
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=18, out_features=14, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=14, out_features=2, bias=True)
)
No. of inputs : 18
No. of nodes : 14
No. of parameters : 296
Voting weight:  [1.0]
Mean Accuracy:  75.51875875875876
Std Accuracy:  4.472293132915998
Hidden Node mean 6.468668668668669
Hidden Node std:  3.2124895982318877
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
Infinite Delay
 99% (1999 of 2000) |################### | Elapsed Time: 0:00:59 ETA:   0:00:00

=== Performance result ===
Accuracy:  51.704102051025515 (+/-) 1.5705720390531992
Testing Loss:  0.691420068229181 (+/-) 0.0017208317438658863
Precision:  0.5682315454056828
Recall:  0.5170410205102551
F1 score:  0.4757090417128868
Testing Time:  0.012343252820334117 (+/-) 0.010820442847535144
Training Time:  9.91840372090342e-07 (+/-) 3.1341736724551546e-05


=== Average network evolution ===
Total hidden node:  5.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=18, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 18
No. of nodes : 5
No. of parameters : 107
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:00:59 ETA:  00:00:00

=== Performance result ===
Accuracy:  61.60035017508755 (+/-) 1.510117579856355
Testing Loss:  0.6820684604909553 (+/-) 0.0014671437301067359
Precision:  0.6441095627165845
Recall:  0.6160035017508755
F1 score:  0.5728519118628765
Testing Time:  0.012120761532614146 (+/-) 0.010620884366524311
Training Time:  1.5048160142693417e-06 (+/-) 3.881664672769537e-05


=== Average network evolution ===
Total hidden node:  4.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=18, out_features=4, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 18
No. of nodes : 4
No. of parameters : 86
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:00:59 ETA:  00:00:00

=== Performance result ===
Accuracy:  54.66093046523262 (+/-) 1.5733871450540848
Testing Loss:  0.6883744914988508 (+/-) 0.0025939343100569497
Precision:  0.5859419653270574
Recall:  0.5466093046523262
F1 score:  0.4005888579984073
Testing Time:  0.01207279741555348 (+/-) 0.010314057036444089
Training Time:  4.980670266774012e-07 (+/-) 2.2263094677075055e-05


=== Average network evolution ===
Total hidden node:  3.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=18, out_features=3, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 18
No. of nodes : 3
No. of parameters : 65
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:01:00 ETA:  00:00:00

=== Performance result ===
Accuracy:  54.23906953476739 (+/-) 1.5656643916117252
Testing Loss:  0.6898041681804438 (+/-) 0.004756907121193263
Precision:  0.7517972853190777
Recall:  0.5423906953476738
F1 score:  0.38147134384025033
Testing Time:  0.01216755717202626 (+/-) 0.010738612511248037
Training Time:  2.496417848511658e-06 (+/-) 4.9853420957717e-05


=== Average network evolution ===
Total hidden node:  3.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=18, out_features=3, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 18
No. of nodes : 3
No. of parameters : 65
Voting weight:  [1.0]
100% (2000 of 2000) |####################| Elapsed Time: 0:00:59 ETA:  00:00:00

=== Performance result ===
Accuracy:  49.4816408204102 (+/-) 1.6060379160992748
Testing Loss:  0.7006591322303951 (+/-) 0.003000627388455331
Precision:  0.5123545696479324
Recall:  0.49481640820410205
F1 score:  0.4838415832199744
Testing Time:  0.012373345204745012 (+/-) 0.01058769446706073
Training Time:  1.9963232501260395e-06 (+/-) 4.458332414952005e-05


=== Average network evolution ===
Total hidden node:  5.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=18, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 18
No. of nodes : 5
No. of parameters : 107
Voting weight:  [1.0]
Mean Accuracy:  54.33674674674675
Std Accuracy:  4.372474191745951
Hidden Node mean 4.0
Hidden Node std:  0.8944271909999159
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
In [9]:
%run ADL_electricitypricing.ipynb
Number of input:  8
Number of output:  2
Number of batch:  45
All labeled
100% (45 of 45) |########################| Elapsed Time: 0:01:44 ETA:  00:00:00

=== Performance result ===
Accuracy:  57.81363636363638 (+/-) 7.3946415153471685
Testing Loss:  0.6708605275912718 (+/-) 0.02272830281356067
Precision:  0.5520166250533703
Recall:  0.5781363636363637
F1 score:  0.505815291734194
Testing Time:  0.010455792600458319 (+/-) 0.013123078416592156
Training Time:  2.366582301529971 (+/-) 0.7818014347170686


=== Average network evolution ===
Total hidden node:  14.704545454545455 (+/-) 5.3664765207661045
Number of layer:  3.409090909090909 (+/-) 1.2120265114521258


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=3, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 3
No. of parameters : 35
basicNet(
  (linear): Linear(in_features=3, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 3
No. of nodes : 5
No. of parameters : 32
basicNet(
  (linear): Linear(in_features=5, out_features=4, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 4
No. of parameters : 34
basicNet(
  (linear): Linear(in_features=4, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 4
No. of nodes : 5
No. of parameters : 37
basicNet(
  (linear): Linear(in_features=5, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 5
No. of parameters : 42
Voting weight:  [0.0, 0.4997183008050181, 0.0, 0.0005633983899638098, 0.4997183008050181]
100% (45 of 45) |########################| Elapsed Time: 0:01:12 ETA:  00:00:00

=== Performance result ===
Accuracy:  61.575 (+/-) 8.036822217993649
Testing Loss:  0.6335059851408005 (+/-) 0.048896460520388214
Precision:  0.6063829287763294
Recall:  0.61575
F1 score:  0.5996802904492604
Testing Time:  0.005687800320712003 (+/-) 0.009406490600081889
Training Time:  1.6436270150271328 (+/-) 0.30126986238035397


=== Average network evolution ===
Total hidden node:  5.931818181818182 (+/-) 3.557308272944634
Number of layer:  1.2954545454545454 (+/-) 0.6244832574560949


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=4, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 4
No. of parameters : 46
Voting weight:  [1.0]
100% (45 of 45) |########################| Elapsed Time: 0:01:08 ETA:  00:00:00

=== Performance result ===
Accuracy:  65.56136363636364 (+/-) 6.5602181196100116
Testing Loss:  0.6132604879411784 (+/-) 0.05731778206984663
Precision:  0.6505456957294925
Recall:  0.6556136363636363
F1 score:  0.6489131586779373
Testing Time:  0.00440128283067183 (+/-) 0.007439060744968732
Training Time:  1.5523505861108953 (+/-) 0.1018618025738576


=== Average network evolution ===
Total hidden node:  7.795454545454546 (+/-) 1.531184114791043
Number of layer:  1.0454545454545454 (+/-) 0.20829889522526546


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=9, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 9
No. of parameters : 101
Voting weight:  [1.0]
100% (45 of 45) |########################| Elapsed Time: 0:01:18 ETA:  00:00:00

=== Performance result ===
Accuracy:  64.81136363636364 (+/-) 7.0479595986450025
Testing Loss:  0.6253216090527448 (+/-) 0.05958365567573209
Precision:  0.6425676907879293
Recall:  0.6481136363636364
F1 score:  0.636075702408277
Testing Time:  0.005960041826421564 (+/-) 0.009806372427248649
Training Time:  1.784017730842937 (+/-) 0.37014210524735786


=== Average network evolution ===
Total hidden node:  9.909090909090908 (+/-) 3.308516433650517
Number of layer:  1.3863636363636365 (+/-) 0.5727092349251888


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=9, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 9
No. of parameters : 101
basicNet(
  (linear): Linear(in_features=9, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 9
No. of nodes : 5
No. of parameters : 62
Voting weight:  [0.0, 1.0]
100% (45 of 45) |########################| Elapsed Time: 0:01:15 ETA:  00:00:00

=== Performance result ===
Accuracy:  63.974999999999994 (+/-) 8.35624766267731
Testing Loss:  0.6322945749217813 (+/-) 0.0612951913603268
Precision:  0.6371678689361756
Recall:  0.63975
F1 score:  0.6380503117442345
Testing Time:  0.005734107711098411 (+/-) 0.009076347679689857
Training Time:  1.7151612531055103 (+/-) 0.4380454872671639


=== Average network evolution ===
Total hidden node:  11.090909090909092 (+/-) 4.851514205136849
Number of layer:  1.2954545454545454 (+/-) 0.45624681590647115


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=10, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=10, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 10
No. of parameters : 112
basicNet(
  (linear): Linear(in_features=10, out_features=9, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 10
No. of nodes : 9
No. of parameters : 119
Voting weight:  [0.0, 1.0]
Mean Accuracy:  63.103720930232555
Std Accuracy:  7.1020393478820045
Hidden Node mean 9.944186046511629
Hidden Node std:  4.988046284150594
Hidden Layer mean:  1.697674418604651
Hidden Layer std:  1.123426728775396
50% labeled
100% (45 of 45) |########################| Elapsed Time: 0:00:34 ETA:  00:00:00

=== Performance result ===
Accuracy:  63.697727272727256 (+/-) 6.784122125708596
Testing Loss:  0.6220933916893873 (+/-) 0.04814847695145634
Precision:  0.6306950526161613
Recall:  0.6369772727272728
F1 score:  0.6291974124956567
Testing Time:  0.0042202364314686165 (+/-) 0.007456163710133397
Training Time:  0.7865779020569541 (+/-) 0.042849104631892634


=== Average network evolution ===
Total hidden node:  5.545454545454546 (+/-) 0.49792959773196915
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 6
No. of parameters : 68
Voting weight:  [1.0]
100% (45 of 45) |########################| Elapsed Time: 0:00:33 ETA:  00:00:00

=== Performance result ===
Accuracy:  62.92954545454545 (+/-) 6.762919607061009
Testing Loss:  0.6311250478029251 (+/-) 0.06769831661317884
Precision:  0.6217311865134764
Recall:  0.6292954545454545
F1 score:  0.6146636840004508
Testing Time:  0.003327608108520508 (+/-) 0.0010340437788820362
Training Time:  0.7618599425662648 (+/-) 0.012053577899492112


=== Average network evolution ===
Total hidden node:  7.4772727272727275 (+/-) 1.0763709305649654
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=9, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 9
No. of parameters : 101
Voting weight:  [1.0]
100% (45 of 45) |########################| Elapsed Time: 0:00:33 ETA:  00:00:00

=== Performance result ===
Accuracy:  60.718181818181826 (+/-) 7.073711663208706
Testing Loss:  0.6457081450657411 (+/-) 0.04940393068538957
Precision:  0.5976560204974379
Recall:  0.6071818181818182
F1 score:  0.5951018902682981
Testing Time:  0.0042848370291969995 (+/-) 0.006391291635861934
Training Time:  0.7617661682042208 (+/-) 0.014046577005405443


=== Average network evolution ===
Total hidden node:  8.931818181818182 (+/-) 0.6535819929340183
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=10, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=10, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 10
No. of parameters : 112
Voting weight:  [1.0]
100% (45 of 45) |########################| Elapsed Time: 0:00:33 ETA:  00:00:00

=== Performance result ===
Accuracy:  61.43181818181818 (+/-) 7.973643308005808
Testing Loss:  0.6310233514417302 (+/-) 0.04674968508080527
Precision:  0.6050271115105956
Recall:  0.6143181818181818
F1 score:  0.6003147269634336
Testing Time:  0.004425997083837336 (+/-) 0.00699895799099366
Training Time:  0.760376209562475 (+/-) 0.01544847042940078


=== Average network evolution ===
Total hidden node:  8.568181818181818 (+/-) 0.6178307826849176
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=8, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 8
No. of parameters : 90
Voting weight:  [1.0]
100% (45 of 45) |########################| Elapsed Time: 0:00:33 ETA:  00:00:00

=== Performance result ===
Accuracy:  59.19772727272728 (+/-) 7.911800411019078
Testing Loss:  0.6503746482458982 (+/-) 0.04174595097621097
Precision:  0.585419452209386
Recall:  0.5919772727272727
F1 score:  0.5867451992902079
Testing Time:  0.003964781761169434 (+/-) 0.006735531375566166
Training Time:  0.7651583335616372 (+/-) 0.011387877545221258


=== Average network evolution ===
Total hidden node:  3.022727272727273 (+/-) 0.14903269373413636
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=3, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 3
No. of parameters : 35
Voting weight:  [1.0]
Mean Accuracy:  61.996744186046506
Std Accuracy:  6.597556884297092
Hidden Node mean 6.702325581395349
Hidden Node std:  2.2792785855925866
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
25% labeled
100% (45 of 45) |########################| Elapsed Time: 0:00:16 ETA:  00:00:00

=== Performance result ===
Accuracy:  60.25454545454545 (+/-) 6.347027025790556
Testing Loss:  0.6448589360172098 (+/-) 0.03462017714248512
Precision:  0.5923980947382137
Recall:  0.6025454545454545
F1 score:  0.5897662554304925
Testing Time:  0.004529812119223855 (+/-) 0.0068081151606404515
Training Time:  0.3800530433654785 (+/-) 0.00865523089244446


=== Average network evolution ===
Total hidden node:  8.590909090909092 (+/-) 0.49166608301781667
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=9, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 9
No. of parameters : 101
Voting weight:  [1.0]
100% (45 of 45) |########################| Elapsed Time: 0:00:16 ETA:  00:00:00

=== Performance result ===
Accuracy:  57.59318181818182 (+/-) 7.641049360564195
Testing Loss:  0.6761251213875684 (+/-) 0.02975795432012827
Precision:  0.5538258765893176
Recall:  0.5759318181818182
F1 score:  0.5354556290635968
Testing Time:  0.0040433569387956095 (+/-) 0.007481319705674717
Training Time:  0.37956480004570703 (+/-) 0.0077733260298382635


=== Average network evolution ===
Total hidden node:  3.8181818181818183 (+/-) 0.4406981688560299
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=3, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 3
No. of parameters : 35
Voting weight:  [1.0]
100% (45 of 45) |########################| Elapsed Time: 0:00:16 ETA:  00:00:00

=== Performance result ===
Accuracy:  59.60454545454545 (+/-) 6.71930578616079
Testing Loss:  0.6570572880181399 (+/-) 0.039321139719617114
Precision:  0.5853152088240164
Recall:  0.5960454545454545
F1 score:  0.5832134726625113
Testing Time:  0.004290976307608865 (+/-) 0.006530079787262215
Training Time:  0.38010282949967816 (+/-) 0.006634936974227754


=== Average network evolution ===
Total hidden node:  7.545454545454546 (+/-) 0.49792959773196915
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=8, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 8
No. of parameters : 90
Voting weight:  [1.0]
100% (45 of 45) |########################| Elapsed Time: 0:00:16 ETA:  00:00:00

=== Performance result ===
Accuracy:  59.6340909090909 (+/-) 6.503004312057977
Testing Loss:  0.660595337098295 (+/-) 0.04019124638781493
Precision:  0.5886636282038722
Recall:  0.5963409090909091
F1 score:  0.5894541064041388
Testing Time:  0.003903665325858376 (+/-) 0.006740014172392363
Training Time:  0.3804617632519115 (+/-) 0.011941400396377564


=== Average network evolution ===
Total hidden node:  6.7272727272727275 (+/-) 0.4453617714151233
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=7, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 7
No. of parameters : 79
Voting weight:  [1.0]
100% (45 of 45) |########################| Elapsed Time: 0:00:16 ETA:  00:00:00

=== Performance result ===
Accuracy:  59.852272727272705 (+/-) 5.973596778413686
Testing Loss:  0.6614910133860328 (+/-) 0.03047099591487847
Precision:  0.5854382336772438
Recall:  0.5985227272727273
F1 score:  0.5608488456286037
Testing Time:  0.003682320768182928 (+/-) 0.007062555192163006
Training Time:  0.37675414843992755 (+/-) 0.007640665476902541


=== Average network evolution ===
Total hidden node:  4.5227272727272725 (+/-) 0.4994832039962707
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=4, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 4
No. of parameters : 46
Voting weight:  [1.0]
Mean Accuracy:  59.69395348837209
Std Accuracy:  6.199505659037769
Hidden Node mean 6.232558139534884
Hidden Node std:  1.8731544008464684
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
Infinite Delay
 73% (33 of 45) |#################       | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  56.37954545454546 (+/-) 7.929048422370589
Testing Loss:  0.6843312951651487 (+/-) 0.016688533590044623
Precision:  0.6141323223394426
Recall:  0.5637954545454545
F1 score:  0.5558617184048541
Testing Time:  0.003253053535114635 (+/-) 0.0065461513409619315
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  6.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 6
No. of parameters : 68
Voting weight:  [1.0]
 77% (35 of 45) |##################      | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  58.37272727272728 (+/-) 6.113932064494683
Testing Loss:  0.6735491508787329 (+/-) 0.02473988698809039
Precision:  0.5667684036616671
Recall:  0.5837272727272728
F1 score:  0.4882857918450825
Testing Time:  0.0030154856768521395 (+/-) 0.006419616267122874
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  4.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=4, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 4
No. of parameters : 46
Voting weight:  [1.0]
 97% (44 of 45) |####################### | Elapsed Time: 0:00:00 ETA:   0:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  42.224999999999994 (+/-) 6.415752099325535
Testing Loss:  0.7078339606523514 (+/-) 0.011477615142251374
Precision:  0.1782950625
Recall:  0.42225
F1 score:  0.25072253471611883
Testing Time:  0.001864622939716686 (+/-) 0.0008186299166247629
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  2.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=2, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=2, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 2
No. of parameters : 24
Voting weight:  [1.0]
 62% (28 of 45) |##############          | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  59.875 (+/-) 4.9883101984904314
Testing Loss:  0.6765508136966012 (+/-) 0.007964352247653643
Precision:  0.6121393426221168
Recall:  0.59875
F1 score:  0.6011688329766939
Testing Time:  0.003228967840021307 (+/-) 0.008454607571139713
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  5.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 5
No. of parameters : 57
Voting weight:  [1.0]
100% (45 of 45) |########################| Elapsed Time: 0:00:00 ETA:  00:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  57.775000000000006 (+/-) 6.415752099325535
Testing Loss:  0.6815451668067412 (+/-) 0.024320144526084716
Precision:  0.33379506249999996
Recall:  0.57775
F1 score:  0.4231279511963239
Testing Time:  0.003649592399597168 (+/-) 0.00881343691001829
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  5.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=8, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 5
No. of parameters : 57
Voting weight:  [1.0]
Mean Accuracy:  54.98
Std Accuracy:  8.96195680459585
Hidden Node mean 4.4
Hidden Node std:  1.3564659966250538
Hidden Layer mean:  1.0
Hidden Layer std:  0.0
In [2]:
%run ADL_creditcarddefault.ipynb
Number of input:  24
Number of output:  2
Number of batch:  30
All labeled
100% (30 of 30) |########################| Elapsed Time: 0:00:40 ETA:  00:00:00

=== Performance result ===
Accuracy:  79.3896551724138 (+/-) 2.4256310700497083
Testing Loss:  0.48540904604155444 (+/-) 0.03809364072112745
Precision:  0.776913854983997
Recall:  0.793896551724138
F1 score:  0.7315256663773451
Testing Time:  0.004904968985195817 (+/-) 0.011163449589942528
Training Time:  1.3742512670056573 (+/-) 0.12193839479107552


=== Average network evolution ===
Total hidden node:  9.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=24, out_features=9, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 24
No. of nodes : 9
No. of parameters : 245
Voting weight:  [1.0]
100% (30 of 30) |########################| Elapsed Time: 0:00:40 ETA:  00:00:00

=== Performance result ===
Accuracy:  79.44482758620688 (+/-) 2.461648761591691
Testing Loss:  0.4819189649203728 (+/-) 0.03401801656168297
Precision:  0.7773021350088571
Recall:  0.794448275862069
F1 score:  0.7331888341141605
Testing Time:  0.004325891363209692 (+/-) 0.007647290769928797
Training Time:  1.3800063626519565 (+/-) 0.13415800508112583


=== Average network evolution ===
Total hidden node:  7.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=24, out_features=7, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 24
No. of nodes : 7
No. of parameters : 191
Voting weight:  [1.0]
100% (30 of 30) |########################| Elapsed Time: 0:00:38 ETA:  00:00:00

=== Performance result ===
Accuracy:  79.17241379310343 (+/-) 2.3066465510636625
Testing Loss:  0.4874982916075608 (+/-) 0.035170076520138656
Precision:  0.7696894777362865
Recall:  0.7917241379310345
F1 score:  0.7286301397962517
Testing Time:  0.004690869101162614 (+/-) 0.01100127860645828
Training Time:  1.3074448026459793 (+/-) 0.09246301334111769


=== Average network evolution ===
Total hidden node:  8.655172413793103 (+/-) 0.4753120259341456
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=24, out_features=9, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 24
No. of nodes : 9
No. of parameters : 245
Voting weight:  [1.0]
100% (30 of 30) |########################| Elapsed Time: 0:00:40 ETA:  00:00:00

=== Performance result ===
Accuracy:  79.56206896551723 (+/-) 2.733876090462473
Testing Loss:  0.48361133193147593 (+/-) 0.03706356071810435
Precision:  0.7771364450254247
Recall:  0.7956206896551724
F1 score:  0.7374570867563525
Testing Time:  0.0039767314647806105 (+/-) 0.007942167915063006
Training Time:  1.4069160592967067 (+/-) 0.09628082833015346


=== Average network evolution ===
Total hidden node:  2.2758620689655173 (+/-) 0.44694763437295587
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=24, out_features=2, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=2, out_features=2, bias=True)
)
No. of inputs : 24
No. of nodes : 2
No. of parameters : 56
Voting weight:  [1.0]
100% (30 of 30) |########################| Elapsed Time: 0:00:37 ETA:  00:00:00

=== Performance result ===
Accuracy:  79.4793103448276 (+/-) 2.481292670661643
Testing Loss:  0.4827772820818013 (+/-) 0.035787671050558356
Precision:  0.7758179857184158
Recall:  0.7947931034482759
F1 score:  0.7354864510010255
Testing Time:  0.004394333938072468 (+/-) 0.008634893684487357
Training Time:  1.3015536028763344 (+/-) 0.05212098274112415


=== Average network evolution ===
Total hidden node:  7.724137931034483 (+/-) 0.4469476343729559
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=24, out_features=8, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 24
No. of nodes : 8
No. of parameters : 218
Voting weight:  [1.0]

========== Performance creditcarddefault ==========
Preq Accuracy:  79.41 (+/-) 0.13
F1 score:  0.73 (+/-) 0.0
Precision:  0.78 (+/-) 0.0
Recall:  0.79 (+/-) 0.0
Training time:  1.35 (+/-) 0.04
Testing time:  0.0 (+/-) 0.0


========== Network ==========
Number of hidden layers:  1.0 (+/-) 0.0
Number of features:  7.0 (+/-) 2.61
50% labeled
100% (30 of 30) |########################| Elapsed Time: 0:00:19 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.89655172413794 (+/-) 2.787593543644049
Testing Loss:  0.4922558459742316 (+/-) 0.037597359582854525
Precision:  0.7753999015883385
Recall:  0.7889655172413793
F1 score:  0.7155581449072997
Testing Time:  0.00466130519735402 (+/-) 0.009320271489723789
Training Time:  0.6538175961066937 (+/-) 0.025377348704353365


=== Average network evolution ===
Total hidden node:  10.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=24, out_features=10, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=10, out_features=2, bias=True)
)
No. of inputs : 24
No. of nodes : 10
No. of parameters : 272
Voting weight:  [1.0]
100% (30 of 30) |########################| Elapsed Time: 0:00:19 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.87241379310343 (+/-) 2.7839313001487085
Testing Loss:  0.4971512031966242 (+/-) 0.03808596792324475
Precision:  0.7705951061544142
Recall:  0.7887241379310345
F1 score:  0.7167964790931977
Testing Time:  0.00424931789266652 (+/-) 0.007887688761866943
Training Time:  0.6554858520113188 (+/-) 0.027822346544190846


=== Average network evolution ===
Total hidden node:  9.206896551724139 (+/-) 0.7601864718982275
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=24, out_features=10, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=10, out_features=2, bias=True)
)
No. of inputs : 24
No. of nodes : 10
No. of parameters : 272
Voting weight:  [1.0]
100% (30 of 30) |########################| Elapsed Time: 0:00:19 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.54137931034482 (+/-) 2.2418461052382557
Testing Loss:  0.4964591110574788 (+/-) 0.03663090625051788
Precision:  0.76994416783673
Recall:  0.7854137931034483
F1 score:  0.7052768267891905
Testing Time:  0.004695341504853347 (+/-) 0.010822525236039337
Training Time:  0.6824125257031671 (+/-) 0.0456319824648727


=== Average network evolution ===
Total hidden node:  7.344827586206897 (+/-) 0.4753120259341456
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=24, out_features=8, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 24
No. of nodes : 8
No. of parameters : 218
Voting weight:  [1.0]
100% (30 of 30) |########################| Elapsed Time: 0:00:20 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.81724137931033 (+/-) 2.6206170588856046
Testing Loss:  0.49600325884490176 (+/-) 0.04323257298510106
Precision:  0.7827014310137869
Recall:  0.7881724137931034
F1 score:  0.710711283034088
Testing Time:  0.005172696606866245 (+/-) 0.01185685358429328
Training Time:  0.6977325390125143 (+/-) 0.05963958176416625


=== Average network evolution ===
Total hidden node:  8.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=24, out_features=8, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 24
No. of nodes : 8
No. of parameters : 218
Voting weight:  [1.0]
100% (30 of 30) |########################| Elapsed Time: 0:00:20 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.61724137931034 (+/-) 2.2900186666182805
Testing Loss:  0.49729508367078057 (+/-) 0.03655898527691127
Precision:  0.773085379351646
Recall:  0.7861724137931034
F1 score:  0.7069655964679372
Testing Time:  0.004796398097071154 (+/-) 0.010954202781788826
Training Time:  0.7016903778602337 (+/-) 0.04620810989139147


=== Average network evolution ===
Total hidden node:  6.931034482758621 (+/-) 0.2533954906327425
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=24, out_features=7, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 24
No. of nodes : 7
No. of parameters : 191
Voting weight:  [1.0]

========== Performance creditcarddefault ==========
Preq Accuracy:  78.75 (+/-) 0.14
F1 score:  0.71 (+/-) 0.0
Precision:  0.77 (+/-) 0.0
Recall:  0.79 (+/-) 0.0
Training time:  0.68 (+/-) 0.02
Testing time:  0.0 (+/-) 0.0


========== Network ==========
Number of hidden layers:  1.0 (+/-) 0.0
Number of features:  8.6 (+/-) 1.2
25% labeled
100% (30 of 30) |########################| Elapsed Time: 0:00:10 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.2551724137931 (+/-) 2.6147573510282447
Testing Loss:  0.5008028046838169 (+/-) 0.03829689810008438
Precision:  0.7725041006081595
Recall:  0.7825517241379311
F1 score:  0.6949309475030001
Testing Time:  0.004353432819761079 (+/-) 0.0071086261519850156
Training Time:  0.3419438970500025 (+/-) 0.014907215794696001


=== Average network evolution ===
Total hidden node:  9.862068965517242 (+/-) 0.3448275862068966
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=24, out_features=10, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=10, out_features=2, bias=True)
)
No. of inputs : 24
No. of nodes : 10
No. of parameters : 272
Voting weight:  [1.0]
100% (30 of 30) |########################| Elapsed Time: 0:00:11 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.12758620689657 (+/-) 2.505978582582357
Testing Loss:  0.5080117203038315 (+/-) 0.038214718247245275
Precision:  0.7777589311933805
Recall:  0.7812758620689655
F1 score:  0.6902127758442113
Testing Time:  0.004114192107628132 (+/-) 0.007372065537259306
Training Time:  0.385124790257421 (+/-) 0.04847479697013953


=== Average network evolution ===
Total hidden node:  6.9655172413793105 (+/-) 0.18246560765962697
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=24, out_features=7, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 24
No. of nodes : 7
No. of parameters : 191
Voting weight:  [1.0]
100% (30 of 30) |########################| Elapsed Time: 0:00:09 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.33793103448275 (+/-) 2.636666363513809
Testing Loss:  0.5031268781629102 (+/-) 0.03410298416811261
Precision:  0.7720549229497322
Recall:  0.7833793103448276
F1 score:  0.6977832925987967
Testing Time:  0.004520901318254142 (+/-) 0.009529105061820926
Training Time:  0.3387431605108853 (+/-) 0.014098297091703479


=== Average network evolution ===
Total hidden node:  9.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=24, out_features=9, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 24
No. of nodes : 9
No. of parameters : 245
Voting weight:  [1.0]
100% (30 of 30) |########################| Elapsed Time: 0:00:10 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.02413793103449 (+/-) 2.515970392404844
Testing Loss:  0.5111399991758938 (+/-) 0.03815259725378138
Precision:  0.7655688100903677
Recall:  0.7802413793103449
F1 score:  0.6876942559342658
Testing Time:  0.004995428282639076 (+/-) 0.009644079968857688
Training Time:  0.3644144946131213 (+/-) 0.026812147179565187


=== Average network evolution ===
Total hidden node:  9.689655172413794 (+/-) 0.46263475396547366
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=24, out_features=10, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=10, out_features=2, bias=True)
)
No. of inputs : 24
No. of nodes : 10
No. of parameters : 272
Voting weight:  [1.0]
100% (30 of 30) |########################| Elapsed Time: 0:00:10 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.16896551724139 (+/-) 2.486807044117619
Testing Loss:  0.5055427551269531 (+/-) 0.03750729511547445
Precision:  0.7739529376834158
Recall:  0.7816896551724138
F1 score:  0.6918942605953049
Testing Time:  0.004529344624486463 (+/-) 0.008230167129608657
Training Time:  0.3597124773880531 (+/-) 0.029691113728100817


=== Average network evolution ===
Total hidden node:  8.89655172413793 (+/-) 0.30454347814923605
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=24, out_features=9, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 24
No. of nodes : 9
No. of parameters : 245
Voting weight:  [1.0]

========== Performance creditcarddefault ==========
Preq Accuracy:  78.18 (+/-) 0.11
F1 score:  0.69 (+/-) 0.0
Precision:  0.77 (+/-) 0.0
Recall:  0.78 (+/-) 0.0
Training time:  0.36 (+/-) 0.02
Testing time:  0.0 (+/-) 0.0


========== Network ==========
Number of hidden layers:  1.0 (+/-) 0.0
Number of features:  9.0 (+/-) 1.1
Infinite Delay
100% (30 of 30) |########################| Elapsed Time: 0:00:00 ETA:  00:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1221: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  77.85517241379311 (+/-) 2.505247762115322
Testing Loss:  0.5179515604315132 (+/-) 0.029585884799812553
Precision:  0.6061427871581452
Recall:  0.7785517241379311
F1 score:  0.6816138984678044
Testing Time:  0.0038176980511895543 (+/-) 0.009293943318511539
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  9.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=24, out_features=9, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 24
No. of nodes : 9
No. of parameters : 245
Voting weight:  [1.0]
 23% (7 of 30) |#####                    | Elapsed Time: 0:00:00 ETA:  00:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1221: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  77.85517241379311 (+/-) 2.505247762115322
Testing Loss:  0.5303484386411207 (+/-) 0.024345815101068986
Precision:  0.6061427871581452
Recall:  0.7785517241379311
F1 score:  0.6816138984678044
Testing Time:  0.0030955692817424907 (+/-) 0.00774706523748648
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  12.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=24, out_features=12, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=12, out_features=2, bias=True)
)
No. of inputs : 24
No. of nodes : 12
No. of parameters : 326
Voting weight:  [1.0]
 90% (27 of 30) |#####################   | Elapsed Time: 0:00:00 ETA:   0:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1221: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  77.85517241379311 (+/-) 2.505247762115322
Testing Loss:  0.5301857868145252 (+/-) 0.03649751731048925
Precision:  0.6061427871581452
Recall:  0.7785517241379311
F1 score:  0.6816138984678044
Testing Time:  0.003736841267552869 (+/-) 0.009134597385075535
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  9.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=24, out_features=9, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 24
No. of nodes : 9
No. of parameters : 245
Voting weight:  [1.0]
100% (30 of 30) |########################| Elapsed Time: 0:00:00 ETA:  00:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1221: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  77.85517241379311 (+/-) 2.505247762115322
Testing Loss:  0.5242518535975752 (+/-) 0.027918092537379036
Precision:  0.6061427871581452
Recall:  0.7785517241379311
F1 score:  0.6816138984678044
Testing Time:  0.0035066686827560953 (+/-) 0.007303613424009609
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  10.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=24, out_features=10, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=10, out_features=2, bias=True)
)
No. of inputs : 24
No. of nodes : 10
No. of parameters : 272
Voting weight:  [1.0]
 93% (28 of 30) |######################  | Elapsed Time: 0:00:00 ETA:   0:00:00

=== Performance result ===
Accuracy:  77.85172413793104 (+/-) 2.4994648654372584
Testing Loss:  0.5475063776147777 (+/-) 0.02134287169415684
Precision:  0.694746069179585
Recall:  0.7785172413793103
F1 score:  0.6817272279677457
Testing Time:  0.004071350755362675 (+/-) 0.009477252269679532
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  8.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=24, out_features=8, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 24
No. of nodes : 8
No. of parameters : 218
Voting weight:  [1.0]

========== Performance creditcarddefault ==========
Preq Accuracy:  77.85 (+/-) 0.0
F1 score:  0.68 (+/-) 0.0
Precision:  0.62 (+/-) 0.04
Recall:  0.78 (+/-) 0.0
Training time:  0.0 (+/-) 0.0
Testing time:  0.0 (+/-) 0.0


========== Network ==========
Number of hidden layers:  1.0 (+/-) 0.0
Number of features:  9.6 (+/-) 1.36
In [1]:
%run ADL_occupancy.ipynb
Number of input:  5
Number of output:  2
Number of batch:  20
All labeled
100% (20 of 20) |########################| Elapsed Time: 0:00:46 ETA:  00:00:00

=== Performance result ===
Accuracy:  79.36315789473684 (+/-) 21.316591277967298
Testing Loss:  0.6024326111288055 (+/-) 0.5022846505432366
Precision:  0.8035568230062642
Recall:  0.7936315789473685
F1 score:  0.728141900913471
Testing Time:  0.01353714340611508 (+/-) 0.01505660850428367
Training Time:  2.4085363588835063 (+/-) 1.0346452528844443


=== Average network evolution ===
Total hidden node:  40.05263157894737 (+/-) 22.108521063180774
Number of layer:  5.2105263157894735 (+/-) 2.5869906794620015


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=5, out_features=7, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 7
No. of parameters : 58
basicNet(
  (linear): Linear(in_features=7, out_features=15, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=15, out_features=2, bias=True)
)
No. of inputs : 7
No. of nodes : 15
No. of parameters : 152
basicNet(
  (linear): Linear(in_features=15, out_features=8, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 15
No. of nodes : 8
No. of parameters : 146
basicNet(
  (linear): Linear(in_features=8, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 6
No. of parameters : 68
basicNet(
  (linear): Linear(in_features=6, out_features=7, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 6
No. of nodes : 7
No. of parameters : 65
basicNet(
  (linear): Linear(in_features=7, out_features=7, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 7
No. of nodes : 7
No. of parameters : 72
basicNet(
  (linear): Linear(in_features=7, out_features=13, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=13, out_features=2, bias=True)
)
No. of inputs : 7
No. of nodes : 13
No. of parameters : 132
basicNet(
  (linear): Linear(in_features=13, out_features=4, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 13
No. of nodes : 4
No. of parameters : 66
Voting weight:  [0.0, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5]
100% (20 of 20) |########################| Elapsed Time: 0:00:41 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.89473684210527 (+/-) 21.620201899933907
Testing Loss:  0.5778865523293222 (+/-) 0.5102719055627148
Precision:  0.7636236479942705
Recall:  0.7889473684210526
F1 score:  0.7382051995586141
Testing Time:  0.012738591746280068 (+/-) 0.01581409295519164
Training Time:  2.1933474666193913 (+/-) 0.7422607283012801


=== Average network evolution ===
Total hidden node:  42.421052631578945 (+/-) 19.279975402565892
Number of layer:  4.526315789473684 (+/-) 2.209272827559511


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=5, out_features=15, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=15, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 15
No. of parameters : 122
basicNet(
  (linear): Linear(in_features=15, out_features=26, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=26, out_features=2, bias=True)
)
No. of inputs : 15
No. of nodes : 26
No. of parameters : 470
basicNet(
  (linear): Linear(in_features=26, out_features=7, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 26
No. of nodes : 7
No. of parameters : 205
basicNet(
  (linear): Linear(in_features=7, out_features=4, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=4, out_features=2, bias=True)
)
No. of inputs : 7
No. of nodes : 4
No. of parameters : 42
basicNet(
  (linear): Linear(in_features=4, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 4
No. of nodes : 6
No. of parameters : 44
basicNet(
  (linear): Linear(in_features=6, out_features=11, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=11, out_features=2, bias=True)
)
No. of inputs : 6
No. of nodes : 11
No. of parameters : 101
basicNet(
  (linear): Linear(in_features=11, out_features=3, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 11
No. of nodes : 3
No. of parameters : 44
Voting weight:  [0.0, 0.5, 0.0, 0.0, 0.0, 0.0, 0.5]
100% (20 of 20) |########################| Elapsed Time: 0:00:56 ETA:  00:00:00

=== Performance result ===
Accuracy:  73.94210526315791 (+/-) 23.725986547942114
Testing Loss:  0.6573177707920733 (+/-) 0.532981730478131
Precision:  0.6431281002838621
Recall:  0.739421052631579
F1 score:  0.6730843058959716
Testing Time:  0.014475596578497636 (+/-) 0.01729962848367366
Training Time:  2.9618207906421863 (+/-) 1.566768067577372


=== Average network evolution ===
Total hidden node:  47.31578947368421 (+/-) 26.828995766715074
Number of layer:  5.368421052631579 (+/-) 2.7949301152319483


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=5, out_features=9, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 9
No. of parameters : 74
basicNet(
  (linear): Linear(in_features=9, out_features=13, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=13, out_features=2, bias=True)
)
No. of inputs : 9
No. of nodes : 13
No. of parameters : 158
basicNet(
  (linear): Linear(in_features=13, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 13
No. of nodes : 6
No. of parameters : 98
basicNet(
  (linear): Linear(in_features=6, out_features=8, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 6
No. of nodes : 8
No. of parameters : 74
basicNet(
  (linear): Linear(in_features=8, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 5
No. of parameters : 57
basicNet(
  (linear): Linear(in_features=5, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 6
No. of parameters : 50
basicNet(
  (linear): Linear(in_features=6, out_features=22, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=22, out_features=2, bias=True)
)
No. of inputs : 6
No. of nodes : 22
No. of parameters : 200
basicNet(
  (linear): Linear(in_features=22, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 22
No. of nodes : 6
No. of parameters : 152
basicNet(
  (linear): Linear(in_features=6, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 6
No. of nodes : 5
No. of parameters : 47
basicNet(
  (linear): Linear(in_features=5, out_features=7, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 7
No. of parameters : 58
Voting weight:  [0.0, 0.00024987506246876566, 0.0, 0.0, 0.0, 0.0, 0.0, 0.24987506246876562, 0.24987506246876562, 0.5]
100% (20 of 20) |########################| Elapsed Time: 0:00:49 ETA:  00:00:00

=== Performance result ===
Accuracy:  79.89473684210526 (+/-) 21.352344224419866
Testing Loss:  0.5781744434253165 (+/-) 0.5112402806704262
Precision:  0.8003892825935593
Recall:  0.7989473684210526
F1 score:  0.7425371639881438
Testing Time:  0.012168357246800474 (+/-) 0.015717456890684874
Training Time:  2.591645692524157 (+/-) 0.912847894321539


=== Average network evolution ===
Total hidden node:  39.89473684210526 (+/-) 19.341514491515
Number of layer:  4.526315789473684 (+/-) 2.209272827559511


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=5, out_features=12, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=12, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 12
No. of parameters : 98
basicNet(
  (linear): Linear(in_features=12, out_features=16, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=16, out_features=2, bias=True)
)
No. of inputs : 12
No. of nodes : 16
No. of parameters : 242
basicNet(
  (linear): Linear(in_features=16, out_features=8, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 16
No. of nodes : 8
No. of parameters : 154
basicNet(
  (linear): Linear(in_features=8, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 6
No. of parameters : 68
basicNet(
  (linear): Linear(in_features=6, out_features=7, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 6
No. of nodes : 7
No. of parameters : 65
basicNet(
  (linear): Linear(in_features=7, out_features=13, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=13, out_features=2, bias=True)
)
No. of inputs : 7
No. of nodes : 13
No. of parameters : 132
basicNet(
  (linear): Linear(in_features=13, out_features=3, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=3, out_features=2, bias=True)
)
No. of inputs : 13
No. of nodes : 3
No. of parameters : 50
Voting weight:  [0.0, 0.5, 0.0, 0.0, 0.0, 0.0, 0.5]
100% (20 of 20) |########################| Elapsed Time: 0:00:50 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.4421052631579 (+/-) 21.10822528104235
Testing Loss:  0.6779582000668406 (+/-) 0.7989469552912296
Precision:  0.7526346186977521
Recall:  0.7844210526315789
F1 score:  0.7349475452717785
Testing Time:  0.012276160089593185 (+/-) 0.014050232114699255
Training Time:  2.6550418075762297 (+/-) 1.125776950129026


=== Average network evolution ===
Total hidden node:  38.73684210526316 (+/-) 19.841058188204098
Number of layer:  4.578947368421052 (+/-) 2.2784064023066697


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=5, out_features=9, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=9, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 9
No. of parameters : 74
basicNet(
  (linear): Linear(in_features=9, out_features=16, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=16, out_features=2, bias=True)
)
No. of inputs : 9
No. of nodes : 16
No. of parameters : 194
basicNet(
  (linear): Linear(in_features=16, out_features=8, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=8, out_features=2, bias=True)
)
No. of inputs : 16
No. of nodes : 8
No. of parameters : 154
basicNet(
  (linear): Linear(in_features=8, out_features=2, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=2, out_features=2, bias=True)
)
No. of inputs : 8
No. of nodes : 2
No. of parameters : 24
basicNet(
  (linear): Linear(in_features=2, out_features=10, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=10, out_features=2, bias=True)
)
No. of inputs : 2
No. of nodes : 10
No. of parameters : 52
basicNet(
  (linear): Linear(in_features=10, out_features=10, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=10, out_features=2, bias=True)
)
No. of inputs : 10
No. of nodes : 10
No. of parameters : 132
basicNet(
  (linear): Linear(in_features=10, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 10
No. of nodes : 6
No. of parameters : 80
basicNet(
  (linear): Linear(in_features=6, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 6
No. of nodes : 6
No. of parameters : 56
Voting weight:  [0.0, 0.4995004995004996, 0.0, 0.0, 0.0, 0.0, 0.0004995004995004996, 0.5]

========== Performance creditcarddefault ==========
Preq Accuracy:  78.11 (+/-) 2.14
F1 score:  0.72 (+/-) 0.03
Precision:  0.75 (+/-) 0.06
Recall:  0.78 (+/-) 0.02
Training time:  2.56 (+/-) 0.26
Testing time:  0.01 (+/-) 0.0


========== Network ==========
Number of hidden layers:  8.0 (+/-) 1.1
Number of features:  71.6 (+/-) 8.04
50% labeled
100% (20 of 20) |########################| Elapsed Time: 0:00:13 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.1421052631579 (+/-) 26.163098074024507
Testing Loss:  0.45945386696410806 (+/-) 0.43151298380472075
Precision:  0.7656969211889554
Recall:  0.7814210526315789
F1 score:  0.7711544838693642
Testing Time:  0.005294849998072574 (+/-) 0.01006458088742868
Training Time:  0.7207258249583998 (+/-) 0.0711004179549971


=== Average network evolution ===
Total hidden node:  7.894736842105263 (+/-) 1.943808980275725
Number of layer:  1.105263157894737 (+/-) 0.30689220499185793


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=5, out_features=13, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=13, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 13
No. of parameters : 106
Voting weight:  [1.0]
100% (20 of 20) |########################| Elapsed Time: 0:00:13 ETA:  00:00:00

=== Performance result ===
Accuracy:  82.67894736842105 (+/-) 17.99864260809887
Testing Loss:  0.4395388616739135 (+/-) 0.45794847045749076
Precision:  0.8138033565236272
Recall:  0.8267894736842105
F1 score:  0.8134807373935803
Testing Time:  0.005715319984837582 (+/-) 0.01279211371586753
Training Time:  0.6962569889269377 (+/-) 0.04369946785831234


=== Average network evolution ===
Total hidden node:  7.947368421052632 (+/-) 2.416471638578736
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=5, out_features=14, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=14, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 14
No. of parameters : 114
Voting weight:  [1.0]
100% (20 of 20) |########################| Elapsed Time: 0:00:13 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.67368421052633 (+/-) 23.564086012075403
Testing Loss:  0.44651137743341296 (+/-) 0.4425940797623028
Precision:  0.7712179492723577
Recall:  0.7867368421052632
F1 score:  0.7763664628718513
Testing Time:  0.006992603603162263 (+/-) 0.01235976050741714
Training Time:  0.6854561881015175 (+/-) 0.030143744446296146


=== Average network evolution ===
Total hidden node:  14.631578947368421 (+/-) 3.989598664894153
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=5, out_features=22, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=22, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 22
No. of parameters : 178
Voting weight:  [1.0]
100% (20 of 20) |########################| Elapsed Time: 0:00:12 ETA:  00:00:00

=== Performance result ===
Accuracy:  82.93684210526317 (+/-) 18.144078649181964
Testing Loss:  0.4381990016585118 (+/-) 0.4426637922722925
Precision:  0.8168599814194805
Recall:  0.8293684210526315
F1 score:  0.815852950708329
Testing Time:  0.004508620814273232 (+/-) 0.008365441937548972
Training Time:  0.6772381380984658 (+/-) 0.05873748399571789


=== Average network evolution ===
Total hidden node:  9.736842105263158 (+/-) 2.8808278280850543
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=5, out_features=17, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=17, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 17
No. of parameters : 138
Voting weight:  [1.0]
100% (20 of 20) |########################| Elapsed Time: 0:00:18 ETA:  00:00:00

=== Performance result ===
Accuracy:  73.34210526315789 (+/-) 27.46770957419052
Testing Loss:  0.6483429189477312 (+/-) 0.6793301750054619
Precision:  0.6824332837150368
Recall:  0.733421052631579
F1 score:  0.6985089109428686
Testing Time:  0.005968545612535979 (+/-) 0.010855997621176082
Training Time:  0.9414331034610146 (+/-) 0.09611270242832352


=== Average network evolution ===
Total hidden node:  18.210526315789473 (+/-) 6.970456055218233
Number of layer:  1.894736842105263 (+/-) 0.30689220499185793


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=5, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 5
No. of parameters : 42
basicNet(
  (linear): Linear(in_features=5, out_features=19, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=19, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 19
No. of parameters : 154
Voting weight:  [0.0, 1.0]

========== Performance creditcarddefault ==========
Preq Accuracy:  79.15 (+/-) 3.51
F1 score:  0.78 (+/-) 0.04
Precision:  0.77 (+/-) 0.05
Recall:  0.79 (+/-) 0.04
Training time:  0.74 (+/-) 0.1
Testing time:  0.01 (+/-) 0.0


========== Network ==========
Number of hidden layers:  1.2 (+/-) 0.4
Number of features:  18.0 (+/-) 4.34
25% labeled
100% (20 of 20) |########################| Elapsed Time: 0:00:09 ETA:  00:00:00

=== Performance result ===
Accuracy:  66.11578947368422 (+/-) 30.047370725816027
Testing Loss:  0.7605385647988633 (+/-) 0.6250741495770932
Precision:  0.6181622192982457
Recall:  0.6611578947368421
F1 score:  0.6374759268636577
Testing Time:  0.005909706416882966 (+/-) 0.01038528239880387
Training Time:  0.4916011032305266 (+/-) 0.06351961425483492


=== Average network evolution ===
Total hidden node:  17.57894736842105 (+/-) 4.568954613518043
Number of layer:  1.894736842105263 (+/-) 0.30689220499185793


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=5, out_features=7, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 7
No. of parameters : 58
basicNet(
  (linear): Linear(in_features=7, out_features=14, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=14, out_features=2, bias=True)
)
No. of inputs : 7
No. of nodes : 14
No. of parameters : 142
Voting weight:  [0.0, 1.0]
100% (20 of 20) |########################| Elapsed Time: 0:00:07 ETA:  00:00:00

=== Performance result ===
Accuracy:  80.6263157894737 (+/-) 20.356063162586825
Testing Loss:  0.4822707970773703 (+/-) 0.4385902946140286
Precision:  0.788320890454088
Recall:  0.8062631578947368
F1 score:  0.7741719818993091
Testing Time:  0.002770072535464638 (+/-) 0.0008352738109608479
Training Time:  0.36644915530556127 (+/-) 0.02659829970930831


=== Average network evolution ===
Total hidden node:  7.894736842105263 (+/-) 1.9165412058982236
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=5, out_features=11, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=11, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 11
No. of parameters : 90
Voting weight:  [1.0]
100% (20 of 20) |########################| Elapsed Time: 0:00:09 ETA:  00:00:00

=== Performance result ===
Accuracy:  68.36315789473684 (+/-) 27.864148074585692
Testing Loss:  0.7343697099162168 (+/-) 0.6245150931118894
Precision:  0.6288311990065972
Recall:  0.6836315789473684
F1 score:  0.6520506209915179
Testing Time:  0.003883537493254009 (+/-) 0.0012759864135536202
Training Time:  0.510084026738217 (+/-) 0.06135213907352488


=== Average network evolution ===
Total hidden node:  15.842105263157896 (+/-) 5.470140553324951
Number of layer:  1.894736842105263 (+/-) 0.30689220499185793


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=5, out_features=5, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=5, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 5
No. of parameters : 42
basicNet(
  (linear): Linear(in_features=5, out_features=15, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=15, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 15
No. of parameters : 122
Voting weight:  [0.0, 1.0]
100% (20 of 20) |########################| Elapsed Time: 0:00:06 ETA:  00:00:00

=== Performance result ===
Accuracy:  82.19473684210527 (+/-) 17.911991681088384
Testing Loss:  0.460278516096112 (+/-) 0.43245332072121745
Precision:  0.8075768372734144
Recall:  0.8219473684210526
F1 score:  0.8030812940670305
Testing Time:  0.002820479242425216 (+/-) 0.0008748570909540544
Training Time:  0.3392742182079114 (+/-) 0.012697157016403133


=== Average network evolution ===
Total hidden node:  11.473684210526315 (+/-) 2.256415906339581
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=5, out_features=15, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=15, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 15
No. of parameters : 122
Voting weight:  [1.0]
100% (20 of 20) |########################| Elapsed Time: 0:00:07 ETA:  00:00:00

=== Performance result ===
Accuracy:  77.41052631578947 (+/-) 25.941734201999726
Testing Loss:  0.5181272623962477 (+/-) 0.48915130051920314
Precision:  0.740461229242546
Recall:  0.7741052631578947
F1 score:  0.7443048571271755
Testing Time:  0.004615997013292815 (+/-) 0.009733749383513785
Training Time:  0.3689627898366828 (+/-) 0.028713605933634184


=== Average network evolution ===
Total hidden node:  9.894736842105264 (+/-) 1.9165412058982234
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=5, out_features=13, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=13, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 13
No. of parameters : 106
Voting weight:  [1.0]

========== Performance creditcarddefault ==========
Preq Accuracy:  74.94 (+/-) 6.51
F1 score:  0.72 (+/-) 0.07
Precision:  0.72 (+/-) 0.08
Recall:  0.75 (+/-) 0.07
Training time:  0.42 (+/-) 0.07
Testing time:  0.0 (+/-) 0.0


========== Network ==========
Number of hidden layers:  1.4 (+/-) 0.49
Number of features:  16.0 (+/-) 3.9
Infinite Delay
 60% (12 of 20) |##############          | Elapsed Time: 0:00:00 ETA:  00:00:00

=== Performance result ===
Accuracy:  78.1421052631579 (+/-) 21.1080008724423
Testing Loss:  0.4289956069306323 (+/-) 0.2434010519792057
Precision:  0.8285868310896237
Recall:  0.7814210526315789
F1 score:  0.6953446881554156
Testing Time:  0.005408851723921926 (+/-) 0.010509669398954475
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  12.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=5, out_features=12, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=12, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 12
No. of parameters : 98
Voting weight:  [1.0]
 95% (19 of 20) |######################  | Elapsed Time: 0:00:00 ETA:   0:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1221: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  77.09473684210526 (+/-) 20.698753425068876
Testing Loss:  0.5789694339036942 (+/-) 0.39321772528308785
Precision:  0.5943598448753463
Recall:  0.7709473684210526
F1 score:  0.6712337763095327
Testing Time:  0.005353262549952457 (+/-) 0.012160015263440281
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  7.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=5, out_features=7, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 7
No. of parameters : 58
Voting weight:  [1.0]
100% (20 of 20) |########################| Elapsed Time: 0:00:00 ETA:  00:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1221: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  77.09473684210526 (+/-) 20.698753425068876
Testing Loss:  0.5018552024113504 (+/-) 0.30412484585440563
Precision:  0.5943598448753463
Recall:  0.7709473684210526
F1 score:  0.6712337763095327
Testing Time:  0.0023089961001747533 (+/-) 0.0005569508889111216
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  6.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=5, out_features=6, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=6, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 6
No. of parameters : 50
Voting weight:  [1.0]
N/A% (0 of 20) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1221: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  77.09473684210526 (+/-) 20.698753425068876
Testing Loss:  0.4326793473017843 (+/-) 0.21799056105542558
Precision:  0.5943598448753463
Recall:  0.7709473684210526
F1 score:  0.6712337763095327
Testing Time:  0.0021286763642963612 (+/-) 0.0007573048259582421
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  12.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=5, out_features=12, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=12, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 12
No. of parameters : 98
Voting weight:  [1.0]
 20% (4 of 20) |#####                    | Elapsed Time: 0:00:00 ETA:  00:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1221: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

=== Performance result ===
Accuracy:  77.09473684210526 (+/-) 20.698753425068876
Testing Loss:  0.49313173247011083 (+/-) 0.25128548395747924
Precision:  0.5943598448753463
Recall:  0.7709473684210526
F1 score:  0.6712337763095327
Testing Time:  0.003833193528024774 (+/-) 0.009456542295028791
Training Time:  0.0 (+/-) 0.0


=== Average network evolution ===
Total hidden node:  7.0 (+/-) 0.0
Number of layer:  1.0 (+/-) 0.0


=== Final network structure ===
basicNet(
  (linear): Linear(in_features=5, out_features=7, bias=True)
  (activation): Sigmoid()
  (linearOutput): Linear(in_features=7, out_features=2, bias=True)
)
No. of inputs : 5
No. of nodes : 7
No. of parameters : 58
Voting weight:  [1.0]

========== Performance creditcarddefault ==========
Preq Accuracy:  77.3 (+/-) 0.42
F1 score:  0.68 (+/-) 0.01
Precision:  0.64 (+/-) 0.09
Recall:  0.77 (+/-) 0.0
Training time:  0.0 (+/-) 0.0
Testing time:  0.0 (+/-) 0.0


========== Network ==========
Number of hidden layers:  1.0 (+/-) 0.0
Number of features:  8.8 (+/-) 2.64
In [ ]: